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Sample records for reinforcement-based self-learning fuzzy

  1. Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

    Science.gov (United States)

    Yen, John; Wang, Haojin; Daugherity, Walter C.

    1992-01-01

    Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.

  2. A Neuro-Control Design Based on Fuzzy Reinforcement Learning

    DEFF Research Database (Denmark)

    Katebi, S.D.; Blanke, M.

    This paper describes a neuro-control fuzzy critic design procedure based on reinforcement learning. An important component of the proposed intelligent control configuration is the fuzzy credit assignment unit which acts as a critic, and through fuzzy implications provides adjustment mechanisms....... The fuzzy credit assignment unit comprises a fuzzy system with the appropriate fuzzification, knowledge base and defuzzification components. When an external reinforcement signal (a failure signal) is received, sequences of control actions are evaluated and modified by the action applier unit. The desirable...... ones instruct the neuro-control unit to adjust its weights and are simultaneously stored in the memory unit during the training phase. In response to the internal reinforcement signal (set point threshold deviation), the stored information is retrieved by the action applier unit and utilized for re...

  3. Self-learning fuzzy logic controllers based on reinforcement

    International Nuclear Information System (INIS)

    Wang, Z.; Shao, S.; Ding, J.

    1996-01-01

    This paper proposes a new method for learning and tuning Fuzzy Logic Controllers. The self-learning scheme in this paper is composed of Bucket-Brigade and Genetic Algorithm. The proposed method is tested on the cart-pole system. Simulation results show that our approach has good learning and control performance

  4. GA-based fuzzy reinforcement learning for control of a magnetic bearing system.

    Science.gov (United States)

    Lin, C T; Jou, C P

    2000-01-01

    This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.

  5. Structure identification in fuzzy inference using reinforcement learning

    Science.gov (United States)

    Berenji, Hamid R.; Khedkar, Pratap

    1993-01-01

    In our previous work on the GARIC architecture, we have shown that the system can start with surface structure of the knowledge base (i.e., the linguistic expression of the rules) and learn the deep structure (i.e., the fuzzy membership functions of the labels used in the rules) by using reinforcement learning. Assuming the surface structure, GARIC refines the fuzzy membership functions used in the consequents of the rules using a gradient descent procedure. This hybrid fuzzy logic and reinforcement learning approach can learn to balance a cart-pole system and to backup a truck to its docking location after a few trials. In this paper, we discuss how to do structure identification using reinforcement learning in fuzzy inference systems. This involves identifying both surface as well as deep structure of the knowledge base. The term set of fuzzy linguistic labels used in describing the values of each control variable must be derived. In this process, splitting a label refers to creating new labels which are more granular than the original label and merging two labels creates a more general label. Splitting and merging of labels directly transform the structure of the action selection network used in GARIC by increasing or decreasing the number of hidden layer nodes.

  6. Fuzzy self-learning control for magnetic servo system

    Science.gov (United States)

    Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

    1994-01-01

    It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

  7. Fuzzy OLAP association rules mining-based modular reinforcement learning approach for multiagent systems.

    Science.gov (United States)

    Kaya, Mehmet; Alhajj, Reda

    2005-04-01

    Multiagent systems and data mining have recently attracted considerable attention in the field of computing. Reinforcement learning is the most commonly used learning process for multiagent systems. However, it still has some drawbacks, including modeling other learning agents present in the domain as part of the state of the environment, and some states are experienced much less than others, or some state-action pairs are never visited during the learning phase. Further, before completing the learning process, an agent cannot exhibit a certain behavior in some states that may be experienced sufficiently. In this study, we propose a novel multiagent learning approach to handle these problems. Our approach is based on utilizing the mining process for modular cooperative learning systems. It incorporates fuzziness and online analytical processing (OLAP) based mining to effectively process the information reported by agents. First, we describe a fuzzy data cube OLAP architecture which facilitates effective storage and processing of the state information reported by agents. This way, the action of the other agent, not even in the visual environment. of the agent under consideration, can simply be predicted by extracting online association rules, a well-known data mining technique, from the constructed data cube. Second, we present a new action selection model, which is also based on association rules mining. Finally, we generalize not sufficiently experienced states, by mining multilevel association rules from the proposed fuzzy data cube. Experimental results obtained on two different versions of a well-known pursuit domain show the robustness and effectiveness of the proposed fuzzy OLAP mining based modular learning approach. Finally, we tested the scalability of the approach presented in this paper and compared it with our previous work on modular-fuzzy Q-learning and ordinary Q-learning.

  8. Systems control with generalized probabilistic fuzzy-reinforcement learning

    NARCIS (Netherlands)

    Hinojosa, J.; Nefti, S.; Kaymak, U.

    2011-01-01

    Reinforcement learning (RL) is a valuable learning method when the systems require a selection of control actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems and RL, the environment is considered to be

  9. Sensitivity-based self-learning fuzzy logic control for a servo system

    NARCIS (Netherlands)

    Balenovic, M.

    1998-01-01

    Describes an experimental verification of a self-learning fuzzy logic controller (SLFLC). The SLFLC contains a learning algorithm that utilizes a second-order reference model and a sensitivity model related to the fuzzy controller parameters. The effectiveness of the proposed controller has been

  10. Rule-bases construction through self-learning for a table-based Sugeno-Takagi fuzzy logic control system

    Directory of Open Access Journals (Sweden)

    C. Boldisor

    2009-12-01

    Full Text Available A self-learning based methodology for building the rule-base of a fuzzy logic controller (FLC is presented and verified, aiming to engage intelligent characteristics to a fuzzy logic control systems. The methodology is a simplified version of those presented in today literature. Some aspects are intentionally ignored since it rarely appears in control system engineering and a SISO process is considered here. The fuzzy inference system obtained is a table-based Sugeno-Takagi type. System’s desired performance is defined by a reference model and rules are extracted from recorded data, after the correct control actions are learned. The presented algorithm is tested in constructing the rule-base of a fuzzy controller for a DC drive application. System’s performances and method’s viability are analyzed.

  11. Self-learning fuzzy controllers based on temporal back propagation

    Science.gov (United States)

    Jang, Jyh-Shing R.

    1992-01-01

    This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.

  12. Self-Paced Prioritized Curriculum Learning With Coverage Penalty in Deep Reinforcement Learning.

    Science.gov (United States)

    Ren, Zhipeng; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Zhipeng Ren; Daoyi Dong; Huaxiong Li; Chunlin Chen; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Ren, Zhipeng

    2018-06-01

    In this paper, a new training paradigm is proposed for deep reinforcement learning using self-paced prioritized curriculum learning with coverage penalty. The proposed deep curriculum reinforcement learning (DCRL) takes the most advantage of experience replay by adaptively selecting appropriate transitions from replay memory based on the complexity of each transition. The criteria of complexity in DCRL consist of self-paced priority as well as coverage penalty. The self-paced priority reflects the relationship between the temporal-difference error and the difficulty of the current curriculum for sample efficiency. The coverage penalty is taken into account for sample diversity. With comparison to deep Q network (DQN) and prioritized experience replay (PER) methods, the DCRL algorithm is evaluated on Atari 2600 games, and the experimental results show that DCRL outperforms DQN and PER on most of these games. More results further show that the proposed curriculum training paradigm of DCRL is also applicable and effective for other memory-based deep reinforcement learning approaches, such as double DQN and dueling network. All the experimental results demonstrate that DCRL can achieve improved training efficiency and robustness for deep reinforcement learning.

  13. A self-learning rule base for command following in dynamical systems

    Science.gov (United States)

    Tsai, Wei K.; Lee, Hon-Mun; Parlos, Alexander

    1992-01-01

    In this paper, a self-learning Rule Base for command following in dynamical systems is presented. The learning is accomplished though reinforcement learning using an associative memory called SAM. The main advantage of SAM is that it is a function approximator with explicit storage of training samples. A learning algorithm patterned after the dynamic programming is proposed. Two artificially created, unstable dynamical systems are used for testing, and the Rule Base was used to generate a feedback control to improve the command following ability of the otherwise uncontrolled systems. The numerical results are very encouraging. The controlled systems exhibit a more stable behavior and a better capability to follow reference commands. The rules resulting from the reinforcement learning are explicitly stored and they can be modified or augmented by human experts. Due to overlapping storage scheme of SAM, the stored rules are similar to fuzzy rules.

  14. Reinforcement Learning Based Data Self-Destruction Scheme for Secured Data Management

    Directory of Open Access Journals (Sweden)

    Young Ki Kim

    2018-04-01

    Full Text Available As technologies and services that leverage cloud computing have evolved, the number of businesses and individuals who use them are increasing rapidly. In the course of using cloud services, as users store and use data that include personal information, research on privacy protection models to protect sensitive information in the cloud environment is becoming more important. As a solution to this problem, a self-destructing scheme has been proposed that prevents the decryption of encrypted user data after a certain period of time using a Distributed Hash Table (DHT network. However, the existing self-destructing scheme does not mention how to set the number of key shares and the threshold value considering the environment of the dynamic DHT network. This paper proposes a method to set the parameters to generate the key shares needed for the self-destructing scheme considering the availability and security of data. The proposed method defines state, action, and reward of the reinforcement learning model based on the similarity of the graph, and applies the self-destructing scheme process by updating the parameter based on the reinforcement learning model. Through the proposed technique, key sharing parameters can be set in consideration of data availability and security in dynamic DHT network environments.

  15. A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative learning.

    Science.gov (United States)

    Tan, Javan; Quek, Chai

    2010-06-01

    Self-organizing neurofuzzy approaches have matured in their online learning of fuzzy-associative structures under time-invariant conditions. To maximize their operative value for online reasoning, these self-sustaining mechanisms must also be able to reorganize fuzzy-associative knowledge in real-time dynamic environments. Hence, it is critical to recognize that they would require self-reorganizational skills to rebuild fluid associative structures when their existing organizations fail to respond well to changing circumstances. In this light, while Hebbian theory (Hebb, 1949) is the basic computational framework for associative learning, it is less attractive for time-variant online learning because it suffers from stability limitations that impedes unlearning. Instead, this paper adopts the Bienenstock-Cooper-Munro (BCM) theory of neurological learning via meta-plasticity principles (Bienenstock et al., 1982) that provides for both online associative and dissociative learning. For almost three decades, BCM theory has been shown to effectively brace physiological evidence of synaptic potentiation (association) and depression (dissociation) into a sound mathematical framework for computational learning. This paper proposes an interpretation of the BCM theory of meta-plasticity for an online self-reorganizing fuzzy-associative learning system to realize online-reasoning capabilities. Experimental findings are twofold: 1) the analysis using S&P-500 stock index illustrated that the self-reorganizing approach could follow the trajectory shifts in the time-variant S&P-500 index for about 60 years, and 2) the benchmark profiles showed that the fuzzy-associative approach yielded comparable results with other fuzzy-precision models with similar online objectives.

  16. Episodic reinforcement learning control approach for biped walking

    Directory of Open Access Journals (Sweden)

    Katić Duško

    2012-01-01

    Full Text Available This paper presents a hybrid dynamic control approach to the realization of humanoid biped robotic walk, focusing on the policy gradient episodic reinforcement learning with fuzzy evaluative feedback. The proposed structure of controller involves two feedback loops: a conventional computed torque controller and an episodic reinforcement learning controller. The reinforcement learning part includes fuzzy information about Zero-Moment- Point errors. Simulation tests using a medium-size 36-DOF humanoid robot MEXONE were performed to demonstrate the effectiveness of our method.

  17. Fuzzy-logic based learning style prediction in e-learning using web ...

    Indian Academy of Sciences (India)

    tion, especially in web environments and proposes to use Fuzzy rules to handle the uncertainty in .... learning in safe and supportive environment ... working of the proposed Fuzzy-logic based learning style prediction in e-learning. Section 4.

  18. Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

    International Nuclear Information System (INIS)

    Peng Yafu; Hsu, C.-F.

    2009-01-01

    This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

  19. Design and implementation of an adaptive critic-based neuro-fuzzy controller on an unmanned bicycle

    OpenAIRE

    Shafiekhani, Ali; Mahjoob, Mohammad J.; Akraminia, Mehdi

    2017-01-01

    Fuzzy critic-based learning forms a reinforcement learning method based on dynamic programming. In this paper, an adaptive critic-based neuro-fuzzy system is presented for an unmanned bicycle. The only information available for the critic agent is the system feedback which is interpreted as the last action performed by the controller in the previous state. The signal produced by the critic agent is used along with the error back propagation to tune (online) conclusion parts of the fuzzy infer...

  20. Magnetic induction of hyperthermia by a modified self-learning fuzzy temperature controller

    Science.gov (United States)

    Wang, Wei-Cheng; Tai, Cheng-Chi

    2017-07-01

    The aim of this study involved developing a temperature controller for magnetic induction hyperthermia (MIH). A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The MIH system generated an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating had few side effects when it was extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with a gain tuning mechanism was implemented to obtain high control performance in a wide range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. In this study, the proposed SLFLC was compared to the classical self-tuning fuzzy logic controller and fuzzy model reference learning control. Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicated that the proposed controller showed greater robustness and excellent adaptability with respect to the temperature control of the MIH system.

  1. Fuzzy comprehensive evaluation model of interuniversity collaborative learning based on network

    Science.gov (United States)

    Wenhui, Ma; Yu, Wang

    2017-06-01

    Learning evaluation is an effective method, which plays an important role in the network education evaluation system. But most of the current network learning evaluation methods still use traditional university education evaluation system, which do not take into account of web-based learning characteristics, and they are difficult to fit the rapid development of interuniversity collaborative learning based on network. Fuzzy comprehensive evaluation method is used to evaluate interuniversity collaborative learning based on the combination of fuzzy theory and analytic hierarchy process. Analytic hierarchy process is used to determine the weight of evaluation factors of each layer and to carry out the consistency check. According to the fuzzy comprehensive evaluation method, we establish interuniversity collaborative learning evaluation mathematical model. The proposed scheme provides a new thought for interuniversity collaborative learning evaluation based on network.

  2. Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles.

    Science.gov (United States)

    Pasquier, M; Quek, C; Toh, M

    2001-10-01

    This paper presents part of our research work concerned with the realisation of an Intelligent Vehicle and the technologies required for its routing, navigation, and control. An automated driver prototype has been developed using a self-organising fuzzy rule-based system (POPFNN-CRI(S)) to model and subsequently emulate human driving expertise. The ability of fuzzy logic to represent vague information using linguistic variables makes it a powerful tool to develop rule-based control systems when an exact working model is not available, as is the case of any vehicle-driving task. Designing a fuzzy system, however, is a complex endeavour, due to the need to define the variables and their associated fuzzy sets, and determine a suitable rule base. Many efforts have thus been devoted to automating this process, yielding the development of learning and optimisation techniques. One of them is the family of POP-FNNs, or Pseudo-Outer Product Fuzzy Neural Networks (TVR, AARS(S), AARS(NS), CRI, Yager). These generic self-organising neural networks developed at the Intelligent Systems Laboratory (ISL/NTU) are based on formal fuzzy mathematical theory and are able to objectively extract a fuzzy rule base from training data. In this application, a driving simulator has been developed, that integrates a detailed model of the car dynamics, complete with engine characteristics and environmental parameters, and an OpenGL-based 3D-simulation interface coupled with driving wheel and accelerator/ brake pedals. The simulator has been used on various road scenarios to record from a human pilot driving data consisting of steering and speed control actions associated to road features. Specifically, the POPFNN-CRI(S) system is used to cluster the data and extract a fuzzy rule base modelling the human driving behaviour. Finally, the effectiveness of the generated rule base has been validated using the simulator in autopilot mode.

  3. Fuzzy comprehensive evaluation model of interuniversity collaborative learning based on network

    Directory of Open Access Journals (Sweden)

    Wenhui Ma

    2017-06-01

    Full Text Available Learning evaluation is an effective method, which plays an important role in the network education evaluation system. But most of the current network learning evaluation methods still use traditional university education evaluation system, which do not take into account of web-based learning characteristics, and they are difficult to fit the rapid development of interuniversity collaborative learning based on network. Fuzzy comprehensive evaluation method is used to evaluate interuniversity collaborative learning based on the combination of fuzzy theory and analytic hierarchy process. Analytic hierarchy process is used to determine the weight of evaluation factors of each layer and to carry out the consistency check. According to the fuzzy comprehensive evaluation method, we establish interuniversity collaborative learning evaluation mathematical model. The proposed scheme provides a new thought for interuniversity collaborative learning evaluation based on network.

  4. SVC control enhancement applying self-learning fuzzy algorithm for islanded microgrid

    Directory of Open Access Journals (Sweden)

    Hossam Gabbar

    2016-03-01

    Full Text Available Maintaining voltage stability, within acceptable levels, for islanded Microgrids (MGs is a challenge due to limited exchange power between generation and loads. This paper proposes an algorithm to enhance the dynamic performance of islanded MGs in presence of load disturbance using Static VAR Compensator (SVC with Fuzzy Model Reference Learning Controller (FMRLC. The proposed algorithm compensates MG nonlinearity via fuzzy membership functions and inference mechanism imbedded in both controller and inverse model. Hence, MG keeps the desired performance as required at any operating condition. Furthermore, the self-learning capability of the proposed control algorithm compensates for grid parameter’s variation even with inadequate information about load dynamics. A reference model was designed to reject bus voltage disturbance with achievable performance by the proposed fuzzy controller. Three simulations scenarios have been presented to investigate effectiveness of proposed control algorithm in improving steady-state and transient performance of islanded MGs. The first scenario conducted without SVC, second conducted with SVC using PID controller and third conducted using FMRLC algorithm. A comparison for results shows ability of proposed control algorithm to enhance disturbance rejection due to learning process.

  5. A model reference and sensitivity model-based self-learning fuzzy logic controller as a solution for control of nonlinear servo systems

    NARCIS (Netherlands)

    Kovacic, Z.; Bogdan, S.; Balenovic, M.

    1999-01-01

    In this paper, the design, simulation and experimental verification of a self-learning fuzzy logic controller (SLFLC) suitable for the control of nonlinear servo systems are described. The SLFLC contains a learning algorithm that utilizes a second-order reference model and a sensitivity model

  6. Airline Passenger Profiling Based on Fuzzy Deep Machine Learning.

    Science.gov (United States)

    Zheng, Yu-Jun; Sheng, Wei-Guo; Sun, Xing-Ming; Chen, Sheng-Yong

    2017-12-01

    Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.

  7. A neural fuzzy controller learning by fuzzy error propagation

    Science.gov (United States)

    Nauck, Detlef; Kruse, Rudolf

    1992-01-01

    In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

  8. Reinforcement Learning in the Game of Othello: Learning Against a Fixed Opponent and Learning from Self-Play

    NARCIS (Netherlands)

    van der Ree, Michiel; Wiering, Marco

    2013-01-01

    This paper compares three strategies in using reinforcement learning algorithms to let an artificial agent learnto play the game of Othello. The three strategies that are compared are: Learning by self-play, learning from playing against a fixed opponent, and learning from playing against a fixed

  9. The Study of Reinforcement Learning for Traffic Self-Adaptive Control under Multiagent Markov Game Environment

    Directory of Open Access Journals (Sweden)

    Lun-Hui Xu

    2013-01-01

    Full Text Available Urban traffic self-adaptive control problem is dynamic and uncertain, so the states of traffic environment are hard to be observed. Efficient agent which controls a single intersection can be discovered automatically via multiagent reinforcement learning. However, in the majority of the previous works on this approach, each agent needed perfect observed information when interacting with the environment and learned individually with less efficient coordination. This study casts traffic self-adaptive control as a multiagent Markov game problem. The design employs traffic signal control agent (TSCA for each signalized intersection that coordinates with neighboring TSCAs. A mathematical model for TSCAs’ interaction is built based on nonzero-sum markov game which has been applied to let TSCAs learn how to cooperate. A multiagent Markov game reinforcement learning approach is constructed on the basis of single-agent Q-learning. This method lets each TSCA learn to update its Q-values under the joint actions and imperfect information. The convergence of the proposed algorithm is analyzed theoretically. The simulation results show that the proposed method is convergent and effective in realistic traffic self-adaptive control setting.

  10. A fuzzy controller with a robust learning function

    International Nuclear Information System (INIS)

    Tanji, Jun-ichi; Kinoshita, Mitsuo

    1987-01-01

    A self-organizing fuzzy controller is able to use linguistic decision rules of control strategy and has a strong adaptive property by virture of its rule learning function. While a simple linguistic description of the learning algorithm first introduced by Procyk, et al. has much flexibility for applications to a wide range of different processes, its detailed formulation, in particular with control stability and learning process convergence, is not clear. In this paper, we describe the formulation of an analytical basis for a self-organizing fuzzy controller by using a method of model reference adaptive control systems (MRACS) for which stability in the adaptive loop is theoretically proven. A detailed formulation is described regarding performance evaluation and rule modification in the rule learning process of the controller. Furthermore, an improved learning algorithm using adaptive rule is proposed. An adaptive rule gives a modification coefficient for a rule change estimating the effect of disturbance occurrence in performance evaluation. The effect of introducing an adaptive rule to improve the learning convergency is described by using a simple iterative formulation. Simulation tests are presented for an application of the proposed self-organizing fuzzy controller to the pressure control system in a Boiling Water Reactor (BWR) plant. Results with the tests confirm the improved learning algorithm has strong convergent properties, even in a very disturbed environment. (author)

  11. Evaluation-Function-based Model-free Adaptive Fuzzy Control

    Directory of Open Access Journals (Sweden)

    Agus Naba

    2016-12-01

    Full Text Available Designs of adaptive fuzzy controllers (AFC are commonly based on the Lyapunov approach, which requires a known model of the controlled plant. They need to consider a Lyapunov function candidate as an evaluation function to be minimized. In this study these drawbacks were handled by designing a model-free adaptive fuzzy controller (MFAFC using an approximate evaluation function defined in terms of the current state, the next state, and the control action. MFAFC considers the approximate evaluation function as an evaluative control performance measure similar to the state-action value function in reinforcement learning. The simulation results of applying MFAFC to the inverted pendulum benchmark verified the proposed scheme’s efficacy.

  12. Design of fuzzy learning control systems for steam generator water level control

    International Nuclear Information System (INIS)

    Park, Gee Yong

    1996-02-01

    descent learning algorithm can provide the stable learning and fast learning speed. For more fast learning speed, the modified momentum is applied to the learning scheme. Fuzzy logic controller with learning algorithm described above is applied to water level control of nuclear steam generator through two learning patterns; one is the off-line learning and the other the on-line learning. Fuzzy logic controller trained off-line is useful in the situation that the controller designer is over-burdened with the tuning works for the fuzzy controller structure and the recorded data from plant operation is rich. In the off-line learning, the desired data is required from the control actions of the plant operator or other controllers such as PI controller. The gradient descent learning algorithm extracts the useful rules among total 343 rules which are generated from the relational product of three controller inputs (7x7x7) and tunes membership functions for controller input domain. In practice, it is almost impossible to tune 343 rules constructed in three input dimensions by trial-and-error method of a human designer. The fuzzy logic controller trained off-line shows the good general mapping capability of controller's input-output relationships and also shows excellent robustness to sudden, large load disturbances. Fuzzy logic controller with on-line learning algorithm, which is called Self-Organizing Fuzzy Logic Controller, constructs the controller structure with no control rules at initial in such a way that it creates control rules and tunes controller input membership functions based on the performance criterion as control action goes on and modifies its control structure when uncertain disturbance is suspected during plant operation. Selected tuning parameters of fuzzy logic controller are updated on-line in the learning algorithm. This control algorithm is divided into two types based on the two performance criteria, i.e., performance index table and performance cost

  13. Evaluation of E-Learning Web Sites Using Fuzzy Axiomatic Design Based Approach

    Directory of Open Access Journals (Sweden)

    2010-04-01

    Full Text Available High quality web site has been generally recognized as a critical enabler to conduct online business. Numerous studies exist in the literature to measure the business performance in relation to web site quality. In this paper, an axiomatic design based approach for fuzzy group decision making is adopted to evaluate the quality of e-learning web sites. Another multi-criteria decision making technique, namely fuzzy TOPSIS, is applied in order to validate the outcome. The methodology proposed in this paper has the advantage of incorporating requirements and enabling reductions in the problem size, as compared to fuzzy TOPSIS. A case study focusing on Turkish e-learning websites is presented, and based on the empirical findings, managerial implications and recommendations for future research are offered.

  14. Prediction of Elastic Constants of the Fuzzy Fibre Reinforced Polymer Using Computational Micromechanics

    Science.gov (United States)

    Pawlik, Marzena; Lu, Yiling

    2018-05-01

    Computational micromechanics is a useful tool to predict properties of carbon fibre reinforced polymers. In this paper, a representative volume element (RVE) is used to investigate a fuzzy fibre reinforced polymer. The fuzzy fibre results from the introduction of nanofillers in the fibre surface. The composite being studied contains three phases, namely: the T650 carbon fibre, the carbon nanotubes (CNTs) reinforced interphase and the epoxy resin EPIKOTE 862. CNTs are radially grown on the surface of the carbon fibre, and thus resultant interphase composed of nanotubes and matrix is transversely isotropic. Transversely isotropic properties of the interphase are numerically implemented in the ANSYS FEM software using element orientation command. Obtained numerical predictions are compared with the available analytical models. It is found that the CNTs interphase significantly increased the transverse mechanical properties of the fuzzy fibre reinforced polymer. This extent of enhancement changes monotonically with the carbon fibre volume fraction. This RVE model enables to investigate different orientation of CNTs in the fuzzy fibre model.

  15. Reinforcement Learning Based Artificial Immune Classifier

    Directory of Open Access Journals (Sweden)

    Mehmet Karakose

    2013-01-01

    Full Text Available One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.

  16. Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

    Science.gov (United States)

    Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

    2017-09-01

    This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

  17. Self tuning fuzzy PID type load and frequency controller

    International Nuclear Information System (INIS)

    Yesil, E.; Guezelkaya, M.; Eksin, I.

    2004-01-01

    In this paper, a self tuning fuzzy PID type controller is proposed for solving the load frequency control (LFC) problem. The fuzzy PID type controller is constructed as a set of control rules, and the control signal is directly deduced from the knowledge base and the fuzzy inference. Moreover, there exists a self tuning mechanism that adjusts the input scaling factor corresponding to the derivative coefficient and the output scaling factor corresponding to the integral coefficient of the PID type fuzzy logic controller in an on-line manner. The self tuning mechanism depends on the peak observer idea, and this idea is modified and adapted to the LFC problem. A two area interconnected system is assumed for demonstrations. The proposed self tuning fuzzy PID type controller has been compared with the fuzzy PID type controller without a self tuning mechanism and the conventional integral controller through some performance indices

  18. A Fuzzy Approach to Classify Learning Disability

    OpenAIRE

    Pooja Manghirmalani; Darshana More; Kavita Jain

    2012-01-01

    The endeavor of this work is to support the special education community in their quest to be with the mainstream. The initial segment of the paper gives an exhaustive study of the different mechanisms of diagnosing learning disability. After diagnosis of learning disability the further classification of learning disability that is dyslexia, dysgraphia or dyscalculia are fuzzy. Hence the paper proposes a model based on Fuzzy Expert System which enables the classification of learning disability...

  19. Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network.

    Science.gov (United States)

    Lin, Yang-Yin; Chang, Jyh-Yeong; Lin, Chin-Teng

    2013-02-01

    This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.

  20. Reinforcement Learning Based Novel Adaptive Learning Framework for Smart Grid Prediction

    Directory of Open Access Journals (Sweden)

    Tian Li

    2017-01-01

    Full Text Available Smart grid is a potential infrastructure to supply electricity demand for end users in a safe and reliable manner. With the rapid increase of the share of renewable energy and controllable loads in smart grid, the operation uncertainty of smart grid has increased briskly during recent years. The forecast is responsible for the safety and economic operation of the smart grid. However, most existing forecast methods cannot account for the smart grid due to the disabilities to adapt to the varying operational conditions. In this paper, reinforcement learning is firstly exploited to develop an online learning framework for the smart grid. With the capability of multitime scale resolution, wavelet neural network has been adopted in the online learning framework to yield reinforcement learning and wavelet neural network (RLWNN based adaptive learning scheme. The simulations on two typical prediction problems in smart grid, including wind power prediction and load forecast, validate the effectiveness and the scalability of the proposed RLWNN based learning framework and algorithm.

  1. Effect of reinforcement learning on coordination of multiangent systems

    Science.gov (United States)

    Bukkapatnam, Satish T. S.; Gao, Greg

    2000-12-01

    For effective coordination of distributed environments involving multiagent systems, learning ability of each agent in the environment plays a crucial role. In this paper, we develop a simple group learning method based on reinforcement, and study its effect on coordination through application to a supply chain procurement scenario involving a computer manufacturer. Here, all parties are represented by self-interested, autonomous agents, each capable of performing specific simple tasks. They negotiate with each other to perform complex tasks and thus coordinate supply chain procurement. Reinforcement learning is intended to enable each agent to reach a best negotiable price within a shortest possible time. Our simulations of the application scenario under different learning strategies reveals the positive effects of reinforcement learning on an agent's as well as the system's performance.

  2. Reference Function Based Spatiotemporal Fuzzy Logic Control Design Using Support Vector Regression Learning

    Directory of Open Access Journals (Sweden)

    Xian-Xia Zhang

    2013-01-01

    Full Text Available This paper presents a reference function based 3D FLC design methodology using support vector regression (SVR learning. The concept of reference function is introduced to 3D FLC for the generation of 3D membership functions (MF, which enhance the capability of the 3D FLC to cope with more kinds of MFs. The nonlinear mathematical expression of the reference function based 3D FLC is derived, and spatial fuzzy basis functions are defined. Via relating spatial fuzzy basis functions of a 3D FLC to kernel functions of an SVR, an equivalence relationship between a 3D FLC and an SVR is established. Therefore, a 3D FLC can be constructed using the learned results of an SVR. Furthermore, the universal approximation capability of the proposed 3D fuzzy system is proven in terms of the finite covering theorem. Finally, the proposed method is applied to a catalytic packed-bed reactor and simulation results have verified its effectiveness.

  3. Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

    DEFF Research Database (Denmark)

    Sousa, Tiago; Pinto, Tiago; Praca, Isabel

    2014-01-01

    This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi...

  4. Ellipsoidal fuzzy learning for smart car platoons

    Science.gov (United States)

    Dickerson, Julie A.; Kosko, Bart

    1993-12-01

    A neural-fuzzy system combined supervised and unsupervised learning to find and tune the fuzzy-rules. An additive fuzzy system approximates a function by covering its graph with fuzzy rules. A fuzzy rule patch can take the form of an ellipsoid in the input-output space. Unsupervised competitive learning found the statistics of data clusters. The covariance matrix of each synaptic quantization vector defined on ellipsoid centered at the centroid of the data cluster. Tightly clustered data gave smaller ellipsoids or more certain rules. Sparse data gave larger ellipsoids or less certain rules. Supervised learning tuned the ellipsoids to improve the approximation. The supervised neural system used gradient descent to find the ellipsoidal fuzzy patches. It locally minimized the mean-squared error of the fuzzy approximation. Hybrid ellipsoidal learning estimated the control surface for a smart car controller.

  5. evaluation of a multi-variable self-learning fuzzy logic controller

    African Journals Online (AJOL)

    Dr Obe

    2003-03-01

    Mar 1, 2003 ... The most challenging aspect of the design of a fuzzy logic controller is ... inaccuracy (or structured uncertainty) and unmodelled ... mathematical analysis on paper is impossible ... output (SISO) system that can self-construct ...

  6. An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties

    International Nuclear Information System (INIS)

    Bahmani-Firouzi, Bahman; Farjah, Ebrahim; Azizipanah-Abarghooee, Rasoul

    2013-01-01

    Renewable energy resources such as wind power plants are playing an ever-increasing role in power generation. This paper extends the dynamic economic emission dispatch problem by incorporating wind power plant. This problem is a multi-objective optimization approach in which total electrical power generation costs and combustion emissions are simultaneously minimized over a short-term time span. A stochastic approach based on scenarios is suggested to model the uncertainty associated with hourly load and wind power forecasts. A roulette wheel technique on the basis of probability distribution functions of load and wind power is implemented to generate scenarios. As a result, the stochastic nature of the suggested problem is emancipated by decomposing it into a set of equivalent deterministic problem. An improved multi-objective particle swarm optimization algorithm is applied to obtain the best expected solutions for the proposed stochastic programming framework. To enhance the overall performance and effectiveness of the particle swarm optimization, a fuzzy adaptive technique, θ-search and self-adaptive learning strategy for velocity updating are used to tune the inertia weight factor and to escape from local optima, respectively. The suggested algorithm goes through the search space in the polar coordinates instead of the Cartesian one; whereby the feasible space is more compact. In order to evaluate the efficiency and feasibility of the suggested framework, it is applied to two test systems with small and large scale characteristics. - Highlights: ► Formulates multi-objective DEED problem under a stochastic programming framework. ► Considers uncertainties related to forecasted values of load demand and wind power. ► Proposes an interactive fuzzy satisfying method based on the novel FSALPSO. ► Presents a new self-adaptive learning strategy to improve original PSO algorithm

  7. Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation.

    Science.gov (United States)

    Bauer, Robert; Fels, Meike; Royter, Vladislav; Raco, Valerio; Gharabaghi, Alireza

    2016-09-01

    Considering self-rated mental effort during neurofeedback may improve training of brain self-regulation. Twenty-one healthy, right-handed subjects performed kinesthetic motor imagery of opening their left hand, while threshold-based classification of beta-band desynchronization resulted in proprioceptive robotic feedback. The experiment consisted of two blocks in a cross-over design. The participants rated their perceived mental effort nine times per block. In the adaptive block, the threshold was adjusted on the basis of these ratings whereas adjustments were carried out at random in the other block. Electroencephalography was used to examine the cortical activation patterns during the training sessions. The perceived mental effort was correlated with the difficulty threshold of neurofeedback training. Adaptive threshold-setting reduced mental effort and increased the classification accuracy and positive predictive value. This was paralleled by an inter-hemispheric cortical activation pattern in low frequency bands connecting the right frontal and left parietal areas. Optimal balance of mental effort was achieved at thresholds significantly higher than maximum classification accuracy. Rating of mental effort is a feasible approach for effective threshold-adaptation during neurofeedback training. Closed-loop adaptation of the neurofeedback difficulty level facilitates reinforcement learning of brain self-regulation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  8. Using fuzzy self-organising maps for safety critical systems

    International Nuclear Information System (INIS)

    Kurd, Zeshan; Kelly, Tim P.

    2007-01-01

    This paper defines a type of constrained artificial neural network (ANN) that enables analytical certification arguments whilst retaining valuable performance characteristics. Previous work has defined a safety lifecycle for ANNs without detailing a specific neural model. Building on this previous work, the underpinning of the devised model is based upon an existing neuro-fuzzy system called the fuzzy self-organising map (FSOM). The FSOM is type of 'hybrid' ANN which allows behaviour to be described qualitatively and quantitatively using meaningful expressions. Safety of the FSOM is argued through adherence to safety requirements-derived from hazard analysis and expressed using safety constraints. The approach enables the construction of compelling (product-based) arguments for mitigation of potential failure modes associated with the FSOM. The constrained FSOM has been termed a 'safety critical artificial neural network' (SCANN). The SCANN can be used for non-linear function approximation and allows certified learning and generalisation for high criticality roles. A discussion of benefits for real-world applications is also presented

  9. Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M.S. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Vesovic, B.V. [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)

    1997-12-01

    This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.

  10. Evaluation of students' perceptions on game based learning program using fuzzy set conjoint analysis

    Science.gov (United States)

    Sofian, Siti Siryani; Rambely, Azmin Sham

    2017-04-01

    An effectiveness of a game based learning (GBL) can be determined from an application of fuzzy set conjoint analysis. The analysis was used due to the fuzziness in determining individual perceptions. This study involved a survey collected from 36 students aged 16 years old of SMK Mersing, Johor who participated in a Mathematics Discovery Camp organized by UKM research group called PRISMatik. The aim of this research was to determine the effectiveness of the module delivered to cultivate interest in mathematics subject in the form of game based learning through different values. There were 11 games conducted for the participants and students' perceptions based on the evaluation of six criteria were measured. A seven-point Likert scale method was used to collect students' preferences and perceptions. This scale represented seven linguistic terms to indicate their perceptions on each module of GBLs. Score of perceptions were transformed into degree of similarity using fuzzy set conjoint analysis. It was found that Geometric Analysis Recreation (GEAR) module was able to increase participant preference corresponded to the six attributes generated. The computations were also made for the other 10 games conducted during the camp. Results found that interest, passion and team work were the strongest values obtained from GBL activities in this camp as participants stated very strongly agreed that these attributes fulfilled their preferences in every module. This was an indicator of efficiency for the program. The evaluation using fuzzy conjoint analysis implicated the successfulness of a fuzzy approach to evaluate students' perceptions toward GBL.

  11. Simple Neuron-Fuzzy Tool for Small Control Devices

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    2008-01-01

    Small control computers, running a kind of Fuzzy controller, are more and more used in many systems from household machines to large industrial systems. The purpose of this paper is firstly to describe a tool that is easy to use for implementing self learning Fuzzy systems, that can be executed...... can be described by four different kinds of membership functions. The output fuzzyfication is based on singletons. The rule base can be written in a natural language. The result of the learning is a new version of the Fuzzy system described in the FuNNy language. A simple shower control example...... is shown.  This example shows that FuNNy is able to control the shower and that the learning is able to optimize the Fuzzy system....

  12. Simulation-based optimization parametric optimization techniques and reinforcement learning

    CERN Document Server

    Gosavi, Abhijit

    2003-01-01

    Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to converg...

  13. A reward optimization method based on action subrewards in hierarchical reinforcement learning.

    Science.gov (United States)

    Fu, Yuchen; Liu, Quan; Ling, Xionghong; Cui, Zhiming

    2014-01-01

    Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are "trial and error" and "related reward." A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of "curse of dimensionality," which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The "curse of dimensionality" problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.

  14. Perception-based Co-evolutionary Reinforcement Learning for UAV Sensor Allocation

    National Research Council Canada - National Science Library

    Berenji, Hamid

    2003-01-01

    .... A Perception-based reasoning approach based on co-evolutionary reinforcement learning was developed for jointly addressing sensor allocation on each individual UAV and allocation of a team of UAVs...

  15. Influence of the Migration Process on the Learning Performances of Fuzzy Knowledge Bases

    DEFF Research Database (Denmark)

    Akrout, Khaled; Baron, Luc; Balazinski, Marek

    2007-01-01

    This paper presents the influence of the process of migration between populations in GENO-FLOU, which is an environment of learning of fuzzy knowledge bases by genetic algorithms. Initially the algorithm did not use the process of migration. For the learning, the algorithm uses a hybrid coding......, binary for the base of rules and real for the data base. This hybrid coding used with a set of specialized operators of reproduction proven to be an effective environment of learning. Simulations were made in this environment by adding a process of migration. While varying the number of populations...

  16. Control of a Quadrotor Using a Smart Self-Tuning Fuzzy PID Controller

    Directory of Open Access Journals (Sweden)

    Deepak Gautam

    2013-11-01

    Full Text Available This paper deals with the modelling, simulation-based controller design and path planning of a four rotor helicopter known as a quadrotor. All the drags, aerodynamic, coriolis and gyroscopic effect are neglected. A Newton-Euler formulation is used to derive the mathematical model. A smart self-tuning fuzzy PID controller based on an EKF algorithm is proposed for the attitude and position control of the quadrotor. The PID gains are tuned using a self-tuning fuzzy algorithm. The self-tuning of fuzzy parameters is achieved based on an EKF algorithm. A smart selection technique and exclusive tuning of active fuzzy parameters is proposed to reduce the computational time. Dijkstra's algorithm is used for path planning in a closed and known environment filled with obstacles and/or boundaries. The Dijkstra algorithm helps avoid obstacle and find the shortest route from a given initial position to the final position.

  17. Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning.

    Science.gov (United States)

    Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien

    2015-01-01

    Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.

  18. Self-tuning fuzzy logic nuclear reactor controller

    International Nuclear Information System (INIS)

    Sharif Heger, A.; Alang-Rashid, N.K.

    1996-01-01

    We present a method for self-tuning of fuzzy logic controllers based on the estimation of the optimum value of the centroids of its output fuzzy set. The method can be implemented on-line and does not require modification of membership functions and control rules. The main features of this method are: the rules are left intact to retain the operator's expertise in the FLC rule base, and the parameters that require any adjustment are identifiable in advance and their number is kept at a minimum. Therefore, the use of this method preserves the control statements in the original form. Results of simulation and actual tests show that this tuning method improves the performance of fuzzy logic controllers in following the desired reactor power level trajectories. In addition, this method demonstrates a similar improvement for power up and power down experiments, based on both simulation and actual case studies. For these experiments, the control rules for the fuzzy logic controller were derived from control statements that expressed the relationships between error, rate of error change, and duration of direction of control rod movements

  19. Online constrained model-based reinforcement learning

    CSIR Research Space (South Africa)

    Van Niekerk, B

    2017-08-01

    Full Text Available Constrained Model-based Reinforcement Learning Benjamin van Niekerk School of Computer Science University of the Witwatersrand South Africa Andreas Damianou∗ Amazon.com Cambridge, UK Benjamin Rosman Council for Scientific and Industrial Research, and School... MULTIPLE SHOOTING Using direct multiple shooting (Bock and Plitt, 1984), problem (1) can be transformed into a structured non- linear program (NLP). First, the time horizon [t0, t0 + T ] is partitioned into N equal subintervals [tk, tk+1] for k = 0...

  20. An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules

    Directory of Open Access Journals (Sweden)

    Antonio

    2012-04-01

    Full Text Available Fuzzy modelling research has traditionally focused on certain types of fuzzy rules. However, the use of alternative rule models could improve the ability of fuzzy systems to represent a specific problem. In this proposal, an extended fuzzy rule model, that can include relations between variables in the antecedent of rules is presented. Furthermore, a learning algorithm based on the iterative genetic approach which is able to represent the knowledge using this model is proposed as well. On the other hand, potential relations among initial variables imply an exponential growth in the feasible rule search space. Consequently, two filters for detecting relevant potential relations are added to the learning algorithm. These filters allows to decrease the search space complexity and increase the algorithm efficiency. Finally, we also present an experimental study to demonstrate the benefits of using fuzzy relational rules.

  1. Evaluation of a Multi-Variable Self-Learning Fuzzy Logic Controller ...

    African Journals Online (AJOL)

    In spite of the usefulness of fuzzy control, its main drawback comes from lack of a systematic control design methodology. The most challenging aspect of the design of a fuzzy logic controller is the elicitation of the control rules for its rule base. In this paper, a scheme capable of elicitation of acceptable rules for multivariable ...

  2. Learning to trade via direct reinforcement.

    Science.gov (United States)

    Moody, J; Saffell, M

    2001-01-01

    We present methods for optimizing portfolios, asset allocations, and trading systems based on direct reinforcement (DR). In this approach, investment decision-making is viewed as a stochastic control problem, and strategies are discovered directly. We present an adaptive algorithm called recurrent reinforcement learning (RRL) for discovering investment policies. The need to build forecasting models is eliminated, and better trading performance is obtained. The direct reinforcement approach differs from dynamic programming and reinforcement algorithms such as TD-learning and Q-learning, which attempt to estimate a value function for the control problem. We find that the RRL direct reinforcement framework enables a simpler problem representation, avoids Bellman's curse of dimensionality and offers compelling advantages in efficiency. We demonstrate how direct reinforcement can be used to optimize risk-adjusted investment returns (including the differential Sharpe ratio), while accounting for the effects of transaction costs. In extensive simulation work using real financial data, we find that our approach based on RRL produces better trading strategies than systems utilizing Q-learning (a value function method). Real-world applications include an intra-daily currency trader and a monthly asset allocation system for the S&P 500 Stock Index and T-Bills.

  3. Fuzzy Sarsa with Focussed Replacing Eligibility Traces for Robust and Accurate Control

    Science.gov (United States)

    Kamdem, Sylvain; Ohki, Hidehiro; Sueda, Naomichi

    Several methods of reinforcement learning in continuous state and action spaces that utilize fuzzy logic have been proposed in recent years. This paper introduces Fuzzy Sarsa(λ), an on-policy algorithm for fuzzy learning that relies on a novel way of computing replacing eligibility traces to accelerate the policy evaluation. It is tested against several temporal difference learning algorithms: Sarsa(λ), Fuzzy Q(λ), an earlier fuzzy version of Sarsa and an actor-critic algorithm. We perform detailed evaluations on two benchmark problems : a maze domain and the cart pole. Results of various tests highlight the strengths and weaknesses of these algorithms and show that Fuzzy Sarsa(λ) outperforms all other algorithms tested for a larger granularity of design and under noisy conditions. It is a highly competitive method of learning in realistic noisy domains where a denser fuzzy design over the state space is needed for a more precise control.

  4. Dynamic Fuzzy Logic-Based Quality of Interaction within Blended-Learning: The Rare and Contemporary Dance Cases

    Science.gov (United States)

    Dias, Sofia B.; Diniz, José A.; Hadjileontiadis, Leontios J.

    2014-01-01

    The combination of the process of pedagogical planning within the Blended (b-) learning environment with the users' quality of interaction ("QoI") with the Learning Management System (LMS) is explored here. The required "QoI" (both for professors and students) is estimated by adopting a fuzzy logic-based modeling approach,…

  5. Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning.

    Science.gov (United States)

    Liu, Weirong; Zhuang, Peng; Liang, Hao; Peng, Jun; Huang, Zhiwu; Weirong Liu; Peng Zhuang; Hao Liang; Jun Peng; Zhiwu Huang; Liu, Weirong; Liang, Hao; Peng, Jun; Zhuang, Peng; Huang, Zhiwu

    2018-06-01

    Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

  6. The "proactive" model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept.

    Science.gov (United States)

    Zsuga, Judit; Biro, Klara; Papp, Csaba; Tajti, Gabor; Gesztelyi, Rudolf

    2016-02-01

    Reinforcement learning (RL) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. RL may be assessed using model-based and model-free approaches. Model-based reinforcement learning involves the amygdala, the hippocampus, and the orbitofrontal cortex (OFC). The model-free system involves the pedunculopontine-tegmental nucleus (PPTgN), the ventral tegmental area (VTA) and the ventral striatum (VS). Based on the functional connectivity of VS, model-free and model based RL systems center on the VS that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. Using the concept of reinforcement learning agent we propose that the VS serves as the value function component of the RL agent. Regarding the model utilized for model-based computations we turned to the proactive brain concept, which offers an ubiquitous function for the default network based on its great functional overlap with contextual associative areas. Hence, by means of the default network the brain continuously organizes its environment into context frames enabling the formulation of analogy-based association that are turned into predictions of what to expect. The OFC integrates reward-related information into context frames upon computing reward expectation by compiling stimulus-reward and context-reward information offered by the amygdala and hippocampus, respectively. Furthermore we suggest that the integration of model-based expectations regarding reward into the value signal is further supported by the efferent of the OFC that reach structures canonical for model-free learning (e.g., the PPTgN, VTA, and VS). (c) 2016 APA, all rights reserved).

  7. Self-assessment procedure using fuzzy sets

    Science.gov (United States)

    Mimi, Fotini

    2000-10-01

    Self-Assessment processes, initiated by a company itself and carried out by its own people, are considered to be the starting point for a regular strategic or operative planning process to ensure a continuous quality improvement. Their importance has increased by the growing relevance and acceptance of international quality awards such as the Malcolm Baldrige National Quality Award, the European Quality Award and the Deming Prize. Especially award winners use the instrument of a systematic and regular Self-Assessment and not only because they have to verify their quality and business results for at least three years. The Total Quality Model of the European Foundation for Quality Management (EFQM), used for the European Quality Award, is the basis for Self-Assessment in Europe. This paper presents a self-assessment supporting method based on a methodology of fuzzy control systems providing an effective means of converting the linguistic approximation into an automatic control strategy. In particular, the elements of the Quality Model mentioned above are interpreted as linguistic variables. The LR-type of a fuzzy interval is used for their representation. The input data has a qualitative character based on empirical investigation and expert knowledge and therefore the base- variables are ordinal scaled. The aggregation process takes place on the basis of a hierarchical structure. Finally, in order to render the use of the method more practical a software system on PC basis is developed and implemented.

  8. Reinforcement learning in supply chains.

    Science.gov (United States)

    Valluri, Annapurna; North, Michael J; Macal, Charles M

    2009-10-01

    Effective management of supply chains creates value and can strategically position companies. In practice, human beings have been found to be both surprisingly successful and disappointingly inept at managing supply chains. The related fields of cognitive psychology and artificial intelligence have postulated a variety of potential mechanisms to explain this behavior. One of the leading candidates is reinforcement learning. This paper applies agent-based modeling to investigate the comparative behavioral consequences of three simple reinforcement learning algorithms in a multi-stage supply chain. For the first time, our findings show that the specific algorithm that is employed can have dramatic effects on the results obtained. Reinforcement learning is found to be valuable in multi-stage supply chains with several learning agents, as independent agents can learn to coordinate their behavior. However, learning in multi-stage supply chains using these postulated approaches from cognitive psychology and artificial intelligence take extremely long time periods to achieve stability which raises questions about their ability to explain behavior in real supply chains. The fact that it takes thousands of periods for agents to learn in this simple multi-agent setting provides new evidence that real world decision makers are unlikely to be using strict reinforcement learning in practice.

  9. Fuzzy stochastic damage mechanics (FSDM based on fuzzy auto-adaptive control theory

    Directory of Open Access Journals (Sweden)

    Ya-jun Wang

    2012-06-01

    Full Text Available In order to fully interpret and describe damage mechanics, the origin and development of fuzzy stochastic damage mechanics were introduced based on the analysis of the harmony of damage, probability, and fuzzy membership in the interval of [0,1]. In a complete normed linear space, it was proven that a generalized damage field can be simulated through β probability distribution. Three kinds of fuzzy behaviors of damage variables were formulated and explained through analysis of the generalized uncertainty of damage variables and the establishment of a fuzzy functional expression. Corresponding fuzzy mapping distributions, namely, the half-depressed distribution, swing distribution, and combined swing distribution, which can simulate varying fuzzy evolution in diverse stochastic damage situations, were set up. Furthermore, through demonstration of the generalized probabilistic characteristics of damage variables, the cumulative distribution function and probability density function of fuzzy stochastic damage variables, which show β probability distribution, were modified according to the expansion principle. The three-dimensional fuzzy stochastic damage mechanical behaviors of the Longtan rolled-concrete dam were examined with the self-developed fuzzy stochastic damage finite element program. The statistical correlation and non-normality of random field parameters were considered comprehensively in the fuzzy stochastic damage model described in this paper. The results show that an initial damage field based on the comprehensive statistical evaluation helps to avoid many difficulties in the establishment of experiments and numerical algorithms for damage mechanics analysis.

  10. Temporal Memory Reinforcement Learning for the Autonomous Micro-mobile Robot Based-behavior

    Institute of Scientific and Technical Information of China (English)

    Yang Yujun(杨玉君); Cheng Junshi; Chen Jiapin; Li Xiaohai

    2004-01-01

    This paper presents temporal memory reinforcement learning for the autonomous micro-mobile robot based-behavior. Human being has a memory oblivion process, i.e. the earlier to memorize, the earlier to forget, only the repeated thing can be remembered firmly. Enlightening forms this, and the robot need not memorize all the past states, at the same time economizes the EMS memory space, which is not enough in the MPU of our AMRobot. The proposed algorithm is an extension of the Q-learning, which is an incremental reinforcement learning method. The results of simulation have shown that the algorithm is valid.

  11. Algorithms for Reinforcement Learning

    CERN Document Server

    Szepesvari, Csaba

    2010-01-01

    Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms'

  12. Reinforcement learning or active inference?

    Science.gov (United States)

    Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J

    2009-07-29

    This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

  13. Reinforcement learning or active inference?

    Directory of Open Access Journals (Sweden)

    Karl J Friston

    2009-07-01

    Full Text Available This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

  14. Learning-based diagnosis and repair

    NARCIS (Netherlands)

    Roos, Nico

    2017-01-01

    This paper proposes a new form of diagnosis and repair based on reinforcement learning. Self-interested agents learn locally which agents may provide a low quality of service for a task. The correctness of learned assessments of other agents is proved under conditions on exploration versus

  15. Reinforcement learning in computer vision

    Science.gov (United States)

    Bernstein, A. V.; Burnaev, E. V.

    2018-04-01

    Nowadays, machine learning has become one of the basic technologies used in solving various computer vision tasks such as feature detection, image segmentation, object recognition and tracking. In many applications, various complex systems such as robots are equipped with visual sensors from which they learn state of surrounding environment by solving corresponding computer vision tasks. Solutions of these tasks are used for making decisions about possible future actions. It is not surprising that when solving computer vision tasks we should take into account special aspects of their subsequent application in model-based predictive control. Reinforcement learning is one of modern machine learning technologies in which learning is carried out through interaction with the environment. In recent years, Reinforcement learning has been used both for solving such applied tasks as processing and analysis of visual information, and for solving specific computer vision problems such as filtering, extracting image features, localizing objects in scenes, and many others. The paper describes shortly the Reinforcement learning technology and its use for solving computer vision problems.

  16. Application of a fuzzy control algorithm with improved learning speed to nuclear steam generator level control

    International Nuclear Information System (INIS)

    Park, Gee Yong; Seong, Poong Hyun

    1994-01-01

    In order to reduce the load of tuning works by trial-and-error for obtaining the best control performance of conventional fuzzy control algorithm, a fuzzy control algorithm with learning function is investigated in this work. This fuzzy control algorithm can make its rule base and tune the membership functions automatically by use of learning function which needs the data from the control actions of the plant operator or other controllers. Learning process in fuzzy control algorithm is to find the optimal values of parameters, which consist of the membership functions and the rule base, by gradient descent method. Learning speed of gradient descent is significantly improved in this work with the addition of modified momentum. This control algorithm is applied to the steam generator level control by computer simulations. The simulation results confirm the good performance of this control algorithm for level control and show that the fuzzy learning algorithm has the generalization capability for the relation of inputs and outputs and it also has the excellent capability of disturbance rejection

  17. DYNAMIC AND INCREMENTAL EXPLORATION STRATEGY IN FUSION ADAPTIVE RESONANCE THEORY FOR ONLINE REINFORCEMENT LEARNING

    Directory of Open Access Journals (Sweden)

    Budhitama Subagdja

    2016-06-01

    Full Text Available One of the fundamental challenges in reinforcement learning is to setup a proper balance between exploration and exploitation to obtain the maximum cummulative reward in the long run. Most protocols for exploration bound the overall values to a convergent level of performance. If new knowledge is inserted or the environment is suddenly changed, the issue becomes more intricate as the exploration must compromise the pre-existing knowledge. This paper presents a type of multi-channel adaptive resonance theory (ART neural network model called fusion ART which serves as a fuzzy approximator for reinforcement learning with inherent features that can regulate the exploration strategy. This intrinsic regulation is driven by the condition of the knowledge learnt so far by the agent. The model offers a stable but incremental reinforcement learning that can involve prior rules as bootstrap knowledge for guiding the agent to select the right action. Experiments in obstacle avoidance and navigation tasks demonstrate that in the configuration of learning wherein the agent learns from scratch, the inherent exploration model in fusion ART model is comparable to the basic E-greedy policy. On the other hand, the model is demonstrated to deal with prior knowledge and strike a balance between exploration and exploitation.

  18. Control of suspended low-gravity simulation system based on self-adaptive fuzzy PID

    Science.gov (United States)

    Chen, Zhigang; Qu, Jiangang

    2017-09-01

    In this paper, an active suspended low-gravity simulation system is proposed to follow the vertical motion of the spacecraft. Firstly, working principle and mathematical model of the low-gravity simulation system are shown. In order to establish the balance process and suppress the strong position interference of the system, the idea of self-adaptive fuzzy PID control strategy is proposed. It combines the PID controller with a fuzzy controll strategy, the control system can be automatically adjusted by changing the proportional parameter, integral parameter and differential parameter of the controller in real-time. At last, we use the Simulink tools to verify the performance of the controller. The results show that the system can reach balanced state quickly without overshoot and oscillation by the method of the self-adaptive fuzzy PID, and follow the speed of 3m/s, while simulation degree of accuracy of system can reach to 95.9% or more.

  19. The effects of case-based team learning on students' learning, self regulation and self direction.

    Science.gov (United States)

    Rezaee, Rita; Mosalanejad, Leili

    2015-01-26

    The application of the best approaches to teach adults in medical education is important in the process of training learners to become and remain effective health care providers. This research aims at designing and integrating two approaches, namely team teaching and case study and tries to examine the consequences of these approaches on learning, self regulation and self direction of nursing students. This is a quasi experimental study of 40 students who were taking a course on mental health. The lessons were designed by using two educational techniques: short case based study and team based learning. Data gathering was based on two valid and reliable questionnaires: Self-Directed Readiness Scale (SDLRS) and the self-regulating questionnaire. Open ended questions were also designed for the evaluation of students' with points of view on educational methods. The Results showed an increase in the students' self directed learning based on their performance on the post-test. The results showed that the students' self-directed learning increased after the intervention. The mean difference before and after intervention self management was statistically significant (p=0.0001). Also, self-regulated learning increased with the mean difference after intervention (p=0.001). Other results suggested that case based team learning can have significant effects on increasing students' learning (p=0.003). This article may be of value to medical educators who wish to replace traditional learning with informal learning (student-centered-active learning), so as to enhance not only the students' knowledge, but also the advancement of long- life learning skills.

  20. The Effects of Case-Based Team Learning on Students’ Learning, Self Regulation and Self Direction

    Science.gov (United States)

    Rezaee, Rita; Mosalanejad, Leili

    2015-01-01

    Introduction: The application of the best approaches to teach adults in medical education is important in the process of training learners to become and remain effective health care providers. This research aims at designing and integrating two approaches, namely team teaching and case study and tries to examine the consequences of these approaches on learning, self regulation and self direction of nursing students. Material & Methods: This is aquasi experimental study of 40 students who were taking a course on mental health. The lessons were designed by using two educational techniques: short case based study and team based learning. Data gathering was based on two valid and reliablequestionnaires: Self-Directed Readiness Scale (SDLRS) and the self-regulating questionnaire. Open ended questions were also designed for the evaluation of students’with points of view on educational methods. Results: The Results showed an increase in the students’ self directed learning based on their performance on the post-test. The results showed that the students’ self-directed learning increased after the intervention. The mean difference before and after intervention self management was statistically significant (p=0.0001). Also, self-regulated learning increased with the mean difference after intervention (p=0.001). Other results suggested that case based team learning can have significant effects on increasing students’ learning (p=0.003). Conclusion: This article may be of value to medical educators who wish to replace traditional learning with informal learning (student-centered-active learning), so as to enhance not only the students’ ’knowledge, but also the advancement of long- life learning skills. PMID:25946918

  1. Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

    International Nuclear Information System (INIS)

    Dong Yun Kim; Poong Hyun Seong; .

    1997-01-01

    In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate gains, which minimize the error of system. The proposed algorithm can reduce the time and effort required for obtaining the fuzzy rules through the intelligent learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller. (author)

  2. SCAFFOLDINGAND REINFORCEMENT: USING DIGITAL LOGBOOKS IN LEARNING VOCABULARY

    OpenAIRE

    Khalifa, Salma Hasan Almabrouk; Shabdin, Ahmad Affendi

    2016-01-01

    Reinforcement and scaffolding are tested approaches to enhance learning achievements. Keeping a record of the learning process as well as the new learned words functions as scaffolding to help learners build a comprehensive vocabulary. Similarly, repetitive learning of new words reinforces permanent learning for long-term memory. Paper-based logbooks may prove to be good records of the learning process, but if learners use digital logbooks, the results may be even better. Digital logbooks wit...

  3. The Reinforcement Learning Competition 2014

    OpenAIRE

    Dimitrakakis, Christos; Li, Guangliang; Tziortziotis, Nikoalos

    2014-01-01

    Reinforcement learning is one of the most general problems in artificial intelligence. It has been used to model problems in automated experiment design, control, economics, game playing, scheduling and telecommunications. The aim of the reinforcement learning competition is to encourage the development of very general learning agents for arbitrary reinforcement learning problems and to provide a test-bed for the unbiased evaluation of algorithms.

  4. Now comes the time to defuzzify neuro-fuzzy models

    International Nuclear Information System (INIS)

    Bersini, H.; Bontempi, G.

    1996-01-01

    Fuzzy models present a singular Janus-faced : on one hand, they are knowledge-based software environments constructed from a collection of linguistic IF-THEN rules, and on the other hand, they realize nonlinear mappings which have interesting mathematical properties like low-order interpolation and universal function approximation. Neuro-fuzzy basically provides fuzzy models with the capacity, based on the available data, to compensate for the missing human knowledge by an automatic self-tuning of the structure and the parameters. A first consequence of this hybridization between the architectural and representational aspect of fuzzy models and the learning mechanisms of neural networks has been to progressively increase and fuzzify the contrast between the two Janus faces: readability or performance

  5. Development of fuzzy algorithm with learning function for nuclear steam generator level control

    International Nuclear Information System (INIS)

    Park, Gee Yong; Seong, Poong Hyun

    1993-01-01

    A fuzzy algorithm with learning function is applied to the steam generator level control of nuclear power plant. This algorithm can make its rule base and membership functions suited for steam generator level control by use of the data obtained from the control actions of a skilled operator or of other controllers (i.e., PID controller). The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0 % - 30 % of full power) and the other to level control at high power level (30 % - 100 % of full power). Response time of steam generator level control at low power range with this rule base is shown to be shorter than that of fuzzy controller with direct inference. (Author)

  6. Can model-free reinforcement learning explain deontological moral judgments?

    Science.gov (United States)

    Ayars, Alisabeth

    2016-05-01

    Dual-systems frameworks propose that moral judgments are derived from both an immediate emotional response, and controlled/rational cognition. Recently Cushman (2013) proposed a new dual-system theory based on model-free and model-based reinforcement learning. Model-free learning attaches values to actions based on their history of reward and punishment, and explains some deontological, non-utilitarian judgments. Model-based learning involves the construction of a causal model of the world and allows for far-sighted planning; this form of learning fits well with utilitarian considerations that seek to maximize certain kinds of outcomes. I present three concerns regarding the use of model-free reinforcement learning to explain deontological moral judgment. First, many actions that humans find aversive from model-free learning are not judged to be morally wrong. Moral judgment must require something in addition to model-free learning. Second, there is a dearth of evidence for central predictions of the reinforcement account-e.g., that people with different reinforcement histories will, all else equal, make different moral judgments. Finally, to account for the effect of intention within the framework requires certain assumptions which lack support. These challenges are reasonable foci for future empirical/theoretical work on the model-free/model-based framework. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Adaptive learning fuzzy control of a mobile robot

    International Nuclear Information System (INIS)

    Tsukada, Akira; Suzuki, Katsuo; Fujii, Yoshio; Shinohara, Yoshikuni

    1989-11-01

    In this report a problem is studied to construct a fuzzy controller for a mobile robot to move autonomously along a given reference direction curve, for which control rules are generated and acquired through an adaptive learning process. An adaptive learning fuzzy controller has been developed for a mobile robot. Good properties of the controller are shown through the travelling experiments of the mobile robot. (author)

  8. Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

    Directory of Open Access Journals (Sweden)

    Ahcene Farah

    2002-06-01

    Full Text Available This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles  with more autonomy and intelligence is discussed. Second, the system  for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.

  9. Introduction to fuzzy systems

    CERN Document Server

    Chen, Guanrong

    2005-01-01

    Introduction to Fuzzy Systems provides students with a self-contained introduction that requires no preliminary knowledge of fuzzy mathematics and fuzzy control systems theory. Simplified and readily accessible, it encourages both classroom and self-directed learners to build a solid foundation in fuzzy systems. After introducing the subject, the authors move directly into presenting real-world applications of fuzzy logic, revealing its practical flavor. This practicality is then followed by basic fuzzy systems theory. The book also offers a tutorial on fuzzy control theory, based mainly on th

  10. Fuzzy forecasting based on fuzzy-trend logical relationship groups.

    Science.gov (United States)

    Chen, Shyi-Ming; Wang, Nai-Yi

    2010-10-01

    In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

  11. Self-tuning fuzzy logic nuclear reactor controller

    International Nuclear Information System (INIS)

    Alang-Rashid, N. K.; Heger, A.S.

    1994-01-01

    A method for self-timing of a fuzzy logic controller (FLC) based on the estimation of the optimum value of the centroids of the its output fuzzy sets is proposed. The method can be implemented on-line and does not modify the membership function and the control rules, thus preserving the description of control statements in their original forms. Results of simulation and actual tests show that the tuning method improves the FLCs performance in following desired reactor power level trajectories (simulation tests) and simple power up and power down experiments (simulation and actual tests). The FLC control rules were derived from control statements expressing the relations between error, rate of error change, and control rod duration and direction of movements

  12. Self-tuning fuzzy logic nuclear reactor controller

    Energy Technology Data Exchange (ETDEWEB)

    Alang-Rashid, N K; Heger, A S

    1994-12-31

    A method for self-timing of a fuzzy logic controller (FLC) based on the estimation of the optimum value of the centroids of the its output fuzzy sets is proposed. The method can be implemented on-line and does not modify the membership function and the control rules, thus preserving the description of control statements in their original forms. Results of simulation and actual tests show that the tuning method improves the FLCs performance in following desired reactor power level trajectories (simulation tests) and simple power up and power down experiments (simulation and actual tests). The FLC control rules were derived from control statements expressing the relations between error, rate of error change, and control rod duration and direction of movements.

  13. The effect of discovery learning and problem-based learning on middle school students’ self-regulated learning

    Science.gov (United States)

    Miatun, A.; Muntazhimah

    2018-01-01

    The aim of this research was to determine the effect of learning models on mathematics achievement viewed from student’s self-regulated learning. The learning model compared were discovery learning and problem-based learning. The population was all students at the grade VIII of Junior High School in Boyolali regency. The samples were students of SMPN 4 Boyolali, SMPN 6 Boyolali, and SMPN 4 Mojosongo. The instruments used were mathematics achievement tests and self-regulated learning questionnaire. The data were analyzed using unbalanced two-ways Anova. The conclusion was as follows: (1) discovery learning gives better achievement than problem-based learning. (2) Achievement of students who have high self-regulated learning was better than students who have medium and low self-regulated learning. (3) For discovery learning, achievement of students who have high self-regulated learning was better than students who have medium and low self-regulated learning. For problem-based learning, students who have high and medium self-regulated learning have the same achievement. (4) For students who have high self-regulated learning, discovery learning gives better achievement than problem-based learning. Students who have medium and low self-regulated learning, both learning models give the same achievement.

  14. Effects of team-based learning on self-regulated online learning.

    Science.gov (United States)

    Whittaker, Alice A

    2015-04-10

    Online learning requires higher levels of self-regulation in order to achieve optimal learning outcomes. As nursing education moves further into the blended and online learning venue, new teaching/learning strategies will be required to develop and enhance self-regulated learning skills in nursing students. The purpose of this study was to compare the effectiveness of team-based learning (TBL) with traditional instructor-led (IL) learning, on self-regulated online learning outcomes, in a blended undergraduate research and evidence-based practice course. The nonrandomized sample consisted of 98 students enrolled in the IL control group and 86 students enrolled in the TBL intervention group. The percentage of total possible online viewing time was used as the measure of self-regulated online learning activity. The TBL group demonstrated a significantly higher percentage (p learning activities than the IL control group. The TBL group scored significantly higher on the course examinations (p = 0.003). The findings indicate that TBL is an effective instructional strategy that can be used to achieve the essential outcomes of baccalaureate nursing education by increasing self-regulated learning capabilities in nursing students.

  15. Personalised Learning Object System Based on Self-Regulated Learning Theories

    Directory of Open Access Journals (Sweden)

    Ali Alharbi

    2014-06-01

    Full Text Available Self-regulated learning has become an important construct in education research in the last few years. Selfregulated learning in its simple form is the learner’s ability to monitor and control the learning process. There is increasing research in the literature on how to support students become more self-regulated learners. However, the advancement in the information technology has led to paradigm changes in the design and development of educational content. The concept of learning object instructional technology has emerged as a result of this shift in educational technology paradigms. This paper presents the results of a study that investigated the potential educational effectiveness of a pedagogical framework based on the self-regulated learning theories to support the design of learning object systems to help computer science students. A prototype learning object system was developed based on the contemporary research on self-regulated learning. The system was educationally evaluated in a quasi-experimental study over two semesters in a core programming languages concepts course. The evaluation revealed that a learning object system that takes into consideration contemporary research on self-regulated learning can be an effective learning environment to support computer science education.

  16. A Reinforcement-Based Learning Paradigm Increases Anatomical Learning and Retention-A Neuroeducation Study.

    Science.gov (United States)

    Anderson, Sarah J; Hecker, Kent G; Krigolson, Olave E; Jamniczky, Heather A

    2018-01-01

    In anatomy education, a key hurdle to engaging in higher-level discussion in the classroom is recognizing and understanding the extensive terminology used to identify and describe anatomical structures. Given the time-limited classroom environment, seeking methods to impart this foundational knowledge to students in an efficient manner is essential. Just-in-Time Teaching (JiTT) methods incorporate pre-class exercises (typically online) meant to establish foundational knowledge in novice learners so subsequent instructor-led sessions can focus on deeper, more complex concepts. Determining how best do we design and assess pre-class exercises requires a detailed examination of learning and retention in an applied educational context. Here we used electroencephalography (EEG) as a quantitative dependent variable to track learning and examine the efficacy of JiTT activities to teach anatomy. Specifically, we examined changes in the amplitude of the N250 and reward positivity event-related brain potential (ERP) components alongside behavioral performance as novice students participated in a series of computerized reinforcement-based learning modules to teach neuroanatomical structures. We found that as students learned to identify anatomical structures, the amplitude of the N250 increased and reward positivity amplitude decreased in response to positive feedback. Both on a retention and transfer exercise when learners successfully remembered and translated their knowledge to novel images, the amplitude of the reward positivity remained decreased compared to early learning. Our findings suggest ERPs can be used as a tool to track learning, retention, and transfer of knowledge and that employing the reinforcement learning paradigm is an effective educational approach for developing anatomical expertise.

  17. Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

    International Nuclear Information System (INIS)

    Kim, Dong Yun

    1997-02-01

    In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate adequate gains, which minimize the error of system. The proposed algorithm can reduce the time and efforts required for obtaining the fuzzy rules through the intelligent learning function. The evolutionary programming algorithm is modified and adopted as the method in order to find the optimal gains which are used as the initial gains of FGS with learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller

  18. A Classification Model and an Open E-Learning System Based on Intuitionistic Fuzzy Sets for Instructional Design Concepts

    Science.gov (United States)

    Güyer, Tolga; Aydogdu, Seyhmus

    2016-01-01

    This study suggests a classification model and an e-learning system based on this model for all instructional theories, approaches, models, strategies, methods, and technics being used in the process of instructional design that constitutes a direct or indirect resource for educational technology based on the theory of intuitionistic fuzzy sets…

  19. Determining e-learning success factor in higher education based on user perspective using Fuzzy AHP

    Directory of Open Access Journals (Sweden)

    Anggrainingsih Rini

    2018-01-01

    Full Text Available Recently almost all universities in the world have implemented E-learning to support their academic system. Previous studies have been conducted to determine CSF using Analytic Hierarchy Process (AHP method. However, AHP method cannot handle the uncertainty and vagueness of the human’s opinion, so then it causes less appropriate decision. Some researcher has proposed to use fuzzy sets theory with AHP to increase the ability of AHP to deal problem regarding the uncertainty/fuzziness. This study aims to determine ranks of priorities of the multiple factors which influence the E-learning success using FAHP method. The respondents consist of ten e-learning’s experts, 305 lecturers, and 4195 students at Sebelas Maret University. The result describes similar success factors ranking between both experienced and non-experienced user (lecturer and student. Then, the result shows that there are five most influencial success factors of e-learning at Sebelas Maret University based on the lectures perspective Financial Policy, Regulatory Policy, Course quality, Relevant Content and Technical Support. On the other hand, according to the student's point of view five most e-learning, critical success factors are Quality of Course, Relevant of Content, Completeness of Content, Attitudes toward Student, and Flexibility in taking Course. Therefore, this finding can be used by E-learning management of Sebelas Maret University to deteremine a strategy to to achieve successful implementation of e-learning at Sebelas Maret University with consider these factors.

  20. Continuous residual reinforcement learning for traffic signal control optimization

    NARCIS (Netherlands)

    Aslani, Mohammad; Seipel, Stefan; Wiering, Marco

    2018-01-01

    Traffic signal control can be naturally regarded as a reinforcement learning problem. Unfortunately, it is one of the most difficult classes of reinforcement learning problems owing to its large state space. A straightforward approach to address this challenge is to control traffic signals based on

  1. Autonomous reinforcement learning with experience replay.

    Science.gov (United States)

    Wawrzyński, Paweł; Tanwani, Ajay Kumar

    2013-05-01

    This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the use of previously collected samples, and autonomously estimates the appropriate step-sizes for the learning updates. The algorithm is based on the actor-critic with experience replay whose step-sizes are determined on-line by an enhanced fixed point algorithm for on-line neural network training. An experimental study with simulated octopus arm and half-cheetah demonstrates the feasibility of the proposed algorithm to solve difficult learning control problems in an autonomous way within reasonably short time. Copyright © 2012 Elsevier Ltd. All rights reserved.

  2. Knowledge-Based Reinforcement Learning for Data Mining

    Science.gov (United States)

    Kudenko, Daniel; Grzes, Marek

    Data Mining is the process of extracting patterns from data. Two general avenues of research in the intersecting areas of agents and data mining can be distinguished. The first approach is concerned with mining an agent’s observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent’s actions and goals are often independent of the data mining task. The data collection is mainly considered as a side effect of the agent’s activities. Machine learning techniques applied in such situations fall into the class of supervised learning. In contrast, the second scenario occurs where an agent is actively performing the data mining, and is responsible for the data collection itself. For example, a mobile network agent is acquiring and processing data (where the acquisition may incur a certain cost), or a mobile sensor agent is moving in a (perhaps hostile) environment, collecting and processing sensor readings. In these settings, the tasks of the agent and the data mining are highly intertwined and interdependent (or even identical). Supervised learning is not a suitable technique for these cases. Reinforcement Learning (RL) enables an agent to learn from experience (in form of reward and punishment for explorative actions) and adapt to new situations, without a teacher. RL is an ideal learning technique for these data mining scenarios, because it fits the agent paradigm of continuous sensing and acting, and the RL agent is able to learn to make decisions on the sampling of the environment which provides the data. Nevertheless, RL still suffers from scalability problems, which have prevented its successful use in many complex real-world domains. The more complex the tasks, the longer it takes a reinforcement learning algorithm to converge to a good solution. For many real-world tasks, human expert knowledge is available. For example, human

  3. Value learning through reinforcement : The basics of dopamine and reinforcement learning

    NARCIS (Netherlands)

    Daw, N.D.; Tobler, P.N.; Glimcher, P.W.; Fehr, E.

    2013-01-01

    This chapter provides an overview of reinforcement learning and temporal difference learning and relates these topics to the firing properties of midbrain dopamine neurons. First, we review the RescorlaWagner learning rule and basic learning phenomena, such as blocking, which the rule explains. Then

  4. A Reinforcement-Based Learning Paradigm Increases Anatomical Learning and Retention—A Neuroeducation Study

    Science.gov (United States)

    Anderson, Sarah J.; Hecker, Kent G.; Krigolson, Olave E.; Jamniczky, Heather A.

    2018-01-01

    In anatomy education, a key hurdle to engaging in higher-level discussion in the classroom is recognizing and understanding the extensive terminology used to identify and describe anatomical structures. Given the time-limited classroom environment, seeking methods to impart this foundational knowledge to students in an efficient manner is essential. Just-in-Time Teaching (JiTT) methods incorporate pre-class exercises (typically online) meant to establish foundational knowledge in novice learners so subsequent instructor-led sessions can focus on deeper, more complex concepts. Determining how best do we design and assess pre-class exercises requires a detailed examination of learning and retention in an applied educational context. Here we used electroencephalography (EEG) as a quantitative dependent variable to track learning and examine the efficacy of JiTT activities to teach anatomy. Specifically, we examined changes in the amplitude of the N250 and reward positivity event-related brain potential (ERP) components alongside behavioral performance as novice students participated in a series of computerized reinforcement-based learning modules to teach neuroanatomical structures. We found that as students learned to identify anatomical structures, the amplitude of the N250 increased and reward positivity amplitude decreased in response to positive feedback. Both on a retention and transfer exercise when learners successfully remembered and translated their knowledge to novel images, the amplitude of the reward positivity remained decreased compared to early learning. Our findings suggest ERPs can be used as a tool to track learning, retention, and transfer of knowledge and that employing the reinforcement learning paradigm is an effective educational approach for developing anatomical expertise. PMID:29467638

  5. A Reinforcement-Based Learning Paradigm Increases Anatomical Learning and Retention—A Neuroeducation Study

    Directory of Open Access Journals (Sweden)

    Sarah J. Anderson

    2018-02-01

    Full Text Available In anatomy education, a key hurdle to engaging in higher-level discussion in the classroom is recognizing and understanding the extensive terminology used to identify and describe anatomical structures. Given the time-limited classroom environment, seeking methods to impart this foundational knowledge to students in an efficient manner is essential. Just-in-Time Teaching (JiTT methods incorporate pre-class exercises (typically online meant to establish foundational knowledge in novice learners so subsequent instructor-led sessions can focus on deeper, more complex concepts. Determining how best do we design and assess pre-class exercises requires a detailed examination of learning and retention in an applied educational context. Here we used electroencephalography (EEG as a quantitative dependent variable to track learning and examine the efficacy of JiTT activities to teach anatomy. Specifically, we examined changes in the amplitude of the N250 and reward positivity event-related brain potential (ERP components alongside behavioral performance as novice students participated in a series of computerized reinforcement-based learning modules to teach neuroanatomical structures. We found that as students learned to identify anatomical structures, the amplitude of the N250 increased and reward positivity amplitude decreased in response to positive feedback. Both on a retention and transfer exercise when learners successfully remembered and translated their knowledge to novel images, the amplitude of the reward positivity remained decreased compared to early learning. Our findings suggest ERPs can be used as a tool to track learning, retention, and transfer of knowledge and that employing the reinforcement learning paradigm is an effective educational approach for developing anatomical expertise.

  6. A Simple and Effective Remedial Learning System with a Fuzzy Expert System

    Science.gov (United States)

    Lin, C.-C.; Guo, K.-H.; Lin, Y.-C.

    2016-01-01

    This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate…

  7. Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

    International Nuclear Information System (INIS)

    Kim, Dong Yun; Seong, Poong Hyun

    1996-01-01

    In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller

  8. Reinforcement Learning State-of-the-Art

    CERN Document Server

    Wiering, Marco

    2012-01-01

    Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together the...

  9. Pragmatically Framed Cross-Situational Noun Learning Using Computational Reinforcement Models.

    Science.gov (United States)

    Najnin, Shamima; Banerjee, Bonny

    2018-01-01

    Cross-situational learning and social pragmatic theories are prominent mechanisms for learning word meanings (i.e., word-object pairs). In this paper, the role of reinforcement is investigated for early word-learning by an artificial agent. When exposed to a group of speakers, the agent comes to understand an initial set of vocabulary items belonging to the language used by the group. Both cross-situational learning and social pragmatic theory are taken into account. As social cues, joint attention and prosodic cues in caregiver's speech are considered. During agent-caregiver interaction, the agent selects a word from the caregiver's utterance and learns the relations between that word and the objects in its visual environment. The "novel words to novel objects" language-specific constraint is assumed for computing rewards. The models are learned by maximizing the expected reward using reinforcement learning algorithms [i.e., table-based algorithms: Q-learning, SARSA, SARSA-λ, and neural network-based algorithms: Q-learning for neural network (Q-NN), neural-fitted Q-network (NFQ), and deep Q-network (DQN)]. Neural network-based reinforcement learning models are chosen over table-based models for better generalization and quicker convergence. Simulations are carried out using mother-infant interaction CHILDES dataset for learning word-object pairings. Reinforcement is modeled in two cross-situational learning cases: (1) with joint attention (Attentional models), and (2) with joint attention and prosodic cues (Attentional-prosodic models). Attentional-prosodic models manifest superior performance to Attentional ones for the task of word-learning. The Attentional-prosodic DQN outperforms existing word-learning models for the same task.

  10. Reinforcement function design and bias for efficient learning in mobile robots

    International Nuclear Information System (INIS)

    Touzet, C.; Santos, J.M.

    1998-01-01

    The main paradigm in sub-symbolic learning robot domain is the reinforcement learning method. Various techniques have been developed to deal with the memorization/generalization problem, demonstrating the superior ability of artificial neural network implementations. In this paper, the authors address the issue of designing the reinforcement so as to optimize the exploration part of the learning. They also present and summarize works relative to the use of bias intended to achieve the effective synthesis of the desired behavior. Demonstrative experiments involving a self-organizing map implementation of the Q-learning and real mobile robots (Nomad 200 and Khepera) in a task of obstacle avoidance behavior synthesis are described. 3 figs., 5 tabs

  11. A SELF-ORGANISING FUZZY LOGIC CONTROLLER

    African Journals Online (AJOL)

    ES Obe

    One major drawback of fuzzy logic controllers is the difficulty encountered in the construction of a rule- base ... The greatest limitation of fuzzy logic control is the lack ..... c(kT)= e(kT)-e((k-1)T). (16) .... with the aid of fuzzy models”, It in Industrial.

  12. A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller

    Science.gov (United States)

    Amor, Mohamed Anis Ben; Oda, Takeshi; Watanabe, Shigeyoshi

    This paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver’s approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver’s self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.

  13. Deep Reinforcement Learning: An Overview

    OpenAIRE

    Li, Yuxi

    2017-01-01

    We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsuperv...

  14. Chaotic queue-based genetic algorithm for design of a self-tuning fuzzy logic controller

    Science.gov (United States)

    Saini, Sanju; Saini, J. S.

    2012-11-01

    This paper employs a chaotic queue-based method using logistic equation in a non-canonical genetic algorithm for optimizing the performance of a self-tuning Fuzzy Logic Controller, used for controlling a nonlinear double-coupled system. A comparison has been made with a standard canonical genetic algorithm implemented on the same plant. It has been shown that chaotic queue-method brings an improvement in the performance of the FLC for wide range of set point changes by a more profound initial population spread in the search space.

  15. Analysis of Learning Development With Sugeno Fuzzy Logic And Clustering

    Directory of Open Access Journals (Sweden)

    Maulana Erwin Saputra

    2017-06-01

    Full Text Available In the first journal, I made this attempt to analyze things that affect the achievement of students in each school of course vary. Because students are one of the goals of achieving the goals of successful educational organizations. The mental influence of students’ emotions and behaviors themselves in relation to learning performance. Fuzzy logic can be used in various fields as well as Clustering for grouping, as in Learning Development analyzes. The process will be performed on students based on the symptoms that exist. In this research will use fuzzy logic and clustering. Fuzzy is an uncertain logic but its excess is capable in the process of language reasoning so that in its design is not required complicated mathematical equations. However Clustering method is K-Means method is method where data analysis is broken down by group k (k = 1,2,3, .. k. To know the optimal number of Performance group. The results of the research is with a questionnaire entered into matlab will produce a value that means in generating the graph. And simplify the school in seeing Student performance in the learning process by using certain criteria. So from the system that obtained the results for a decision-making required by the school.

  16. Perceptual learning rules based on reinforcers and attention

    NARCIS (Netherlands)

    Roelfsema, Pieter R.; van Ooyen, Arjen; Watanabe, Takeo

    2010-01-01

    How does the brain learn those visual features that are relevant for behavior? In this article, we focus on two factors that guide plasticity of visual representations. First, reinforcers cause the global release of diffusive neuromodulatory signals that gate plasticity. Second, attentional feedback

  17. A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network

    Directory of Open Access Journals (Sweden)

    Linhui Li

    2015-01-01

    Full Text Available A lateral control method is proposed for intelligent vehicle to track the desired trajectory. Firstly, a lateral control model is established based on the visual preview and dynamic characteristics of intelligent vehicle. Then, the lateral error and orientation error are melded into an integrated error. Considering the system parameter perturbation and the external interference, a sliding model control is introduced in this paper. In order to design a sliding surface, the integrated error is chosen as the parameter of the sliding mode switching function. The sliding mode switching function and its derivative are selected as two inputs of the controller, and the front wheel angle is selected as the output. Next, a fuzzy neural network is established, and the self-learning functions of neural network is utilized to construct the fuzzy rules. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed method.

  18. Rational and Mechanistic Perspectives on Reinforcement Learning

    Science.gov (United States)

    Chater, Nick

    2009-01-01

    This special issue describes important recent developments in applying reinforcement learning models to capture neural and cognitive function. But reinforcement learning, as a theoretical framework, can apply at two very different levels of description: "mechanistic" and "rational." Reinforcement learning is often viewed in mechanistic terms--as…

  19. Reinforcement and inference in cross-situational word learning.

    Science.gov (United States)

    Tilles, Paulo F C; Fontanari, José F

    2013-01-01

    Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm that contains a parameter that controls the strength of the reinforcement applied to associations between concurrent words and referents, and a parameter that regulates inference, which includes built-in biases, such as mutual exclusivity, and information of past learning events. By adjusting these parameters so that the model predictions agree with data from representative experiments on cross-situational word learning, we were able to explain the learning strategies adopted by the participants of those experiments in terms of a trade-off between reinforcement and inference. These strategies can vary wildly depending on the conditions of the experiments. For instance, for fast mapping experiments (i.e., the correct referent could, in principle, be inferred in a single observation) inference is prevalent, whereas for segregated contextual diversity experiments (i.e., the referents are separated in groups and are exhibited with members of their groups only) reinforcement is predominant. Other experiments are explained with more balanced doses of reinforcement and inference.

  20. A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning

    Directory of Open Access Journals (Sweden)

    Bruno A. Angélico

    2013-12-01

    Full Text Available This work analyses the performance of three different population-based metaheuristic approaches applied to Fuzzy cognitive maps (FCM learning in qualitative control of processes. Fuzzy cognitive maps permit to include the previous specialist knowledge in the control rule. Particularly, Particle Swarm Optimization (PSO, Genetic Algorithm (GA and an Ant Colony Optimization (ACO are considered for obtaining appropriate weight matrices for learning the FCM. A statistical convergence analysis within 10000 simulations of each algorithm is presented. In order to validate the proposed approach, two industrial control process problems previously described in the literature are considered in this work.

  1. Smart damping of laminated fuzzy fiber reinforced composite shells using 1–3 piezoelectric composites

    International Nuclear Information System (INIS)

    Kundalwal, S I; Suresh Kumar, R; Ray, M C

    2013-01-01

    This paper deals with the investigation of active constrained layer damping (ACLD) of smart laminated continuous fuzzy fiber reinforced composite (FFRC) shells. The distinct constructional feature of a novel FFRC is that the uniformly spaced short carbon nanotubes (CNTs) are radially grown on the circumferential surfaces of the continuous carbon fiber reinforcements. The constraining layer of the ACLD treatment is considered to be made of vertically/obliquely reinforced 1–3 piezoelectric composite materials. A finite element (FE) model is developed for the laminated FFRC shells integrated with the two patches of the ACLD treatment to investigate the damping characteristics of the laminated FFRC shells. The effect of variation of the orientation angle of the piezoelectric fibers on the damping characteristics of the laminated FFRC shells has been studied when the piezoelectric fibers are coplanar with either of the two mutually orthogonal vertical planes of the piezoelectric composite layer. It is revealed that radial growth of CNTs on the circumferential surfaces of the carbon fibers enhances the attenuation of the amplitude of vibrations and the natural frequencies of the laminated FFRC shells over those of laminated base composite shells without CNTs. (paper)

  2. Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers.

    Directory of Open Access Journals (Sweden)

    Muhammad Ahmad

    Full Text Available Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF, in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN. The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods.

  3. Adaptive Trajectory Tracking Control using Reinforcement Learning for Quadrotor

    Directory of Open Access Journals (Sweden)

    Wenjie Lou

    2016-02-01

    Full Text Available Inaccurate system parameters and unpredicted external disturbances affect the performance of non-linear controllers. In this paper, a new adaptive control algorithm under the reinforcement framework is proposed to stabilize a quadrotor helicopter. Based on a command-filtered non-linear control algorithm, adaptive elements are added and learned by policy-search methods. To predict the inaccurate system parameters, a new kernel-based regression learning method is provided. In addition, Policy learning by Weighting Exploration with the Returns (PoWER and Return Weighted Regression (RWR are utilized to learn the appropriate parameters for adaptive elements in order to cancel the effect of external disturbance. Furthermore, numerical simulations under several conditions are performed, and the ability of adaptive trajectory-tracking control with reinforcement learning are demonstrated.

  4. Improving self-regulated learning junior high school students through computer-based learning

    Science.gov (United States)

    Nurjanah; Dahlan, J. A.

    2018-05-01

    This study is back grounded by the importance of self-regulated learning as an affective aspect that determines the success of students in learning mathematics. The purpose of this research is to see how the improvement of junior high school students' self-regulated learning through computer based learning is reviewed in whole and school level. This research used a quasi-experimental research method. This is because individual sample subjects are not randomly selected. The research design used is Pretest-and-Posttest Control Group Design. Subjects in this study were students of grade VIII junior high school in Bandung taken from high school (A) and middle school (B). The results of this study showed that the increase of the students' self-regulated learning who obtain learning with computer-based learning is higher than students who obtain conventional learning. School-level factors have a significant effect on increasing of the students' self-regulated learning.

  5. Using a board game to reinforce learning.

    Science.gov (United States)

    Yoon, Bona; Rodriguez, Leslie; Faselis, Charles J; Liappis, Angelike P

    2014-03-01

    Experiential gaming strategies offer a variation on traditional learning. A board game was used to present synthesized content of fundamental catheter care concepts and reinforce evidence-based practices relevant to nursing. Board games are innovative educational tools that can enhance active learning. Copyright 2014, SLACK Incorporated.

  6. Determining e-Portfolio Elements in Learning Process Using Fuzzy Delphi Analysis

    Science.gov (United States)

    Mohamad, Syamsul Nor Azlan; Embi, Mohamad Amin; Nordin, Norazah

    2015-01-01

    The present article introduces the Fuzzy Delphi method results obtained in the study on determining e-Portfolio elements in learning process for art and design context. This method bases on qualified experts that assure the validity of the collected information. In particular, the confirmation of elements is based on experts' opinion and…

  7. Bio-robots automatic navigation with graded electric reward stimulation based on Reinforcement Learning.

    Science.gov (United States)

    Zhang, Chen; Sun, Chao; Gao, Liqiang; Zheng, Nenggan; Chen, Weidong; Zheng, Xiaoxiang

    2013-01-01

    Bio-robots based on brain computer interface (BCI) suffer from the lack of considering the characteristic of the animals in navigation. This paper proposed a new method for bio-robots' automatic navigation combining the reward generating algorithm base on Reinforcement Learning (RL) with the learning intelligence of animals together. Given the graded electrical reward, the animal e.g. the rat, intends to seek the maximum reward while exploring an unknown environment. Since the rat has excellent spatial recognition, the rat-robot and the RL algorithm can convergent to an optimal route by co-learning. This work has significant inspiration for the practical development of bio-robots' navigation with hybrid intelligence.

  8. Exploiting Best-Match Equations for Efficient Reinforcement Learning

    NARCIS (Netherlands)

    van Seijen, Harm; Whiteson, Shimon; van Hasselt, Hado; Wiering, Marco

    This article presents and evaluates best-match learning, a new approach to reinforcement learning that trades off the sample efficiency of model-based methods with the space efficiency of model-free methods. Best-match learning works by approximating the solution to a set of best-match equations,

  9. A Day-to-Day Route Choice Model Based on Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Fangfang Wei

    2014-01-01

    Full Text Available Day-to-day traffic dynamics are generated by individual traveler’s route choice and route adjustment behaviors, which are appropriate to be researched by using agent-based model and learning theory. In this paper, we propose a day-to-day route choice model based on reinforcement learning and multiagent simulation. Travelers’ memory, learning rate, and experience cognition are taken into account. Then the model is verified and analyzed. Results show that the network flow can converge to user equilibrium (UE if travelers can remember all the travel time they have experienced, but which is not necessarily the case under limited memory; learning rate can strengthen the flow fluctuation, but memory leads to the contrary side; moreover, high learning rate results in the cyclical oscillation during the process of flow evolution. Finally, both the scenarios of link capacity degradation and random link capacity are used to illustrate the model’s applications. Analyses and applications of our model demonstrate the model is reasonable and useful for studying the day-to-day traffic dynamics.

  10. Reinforcement learning of self-regulated β-oscillations for motor restoration in chronic stroke

    Directory of Open Access Journals (Sweden)

    Georgios eNaros

    2015-07-01

    Full Text Available Neurofeedback training of motor imagery-related brain-states with brain-machine interfaces (BMI is currently being explored prior to standard physiotherapy to improve the motor outcome of stroke rehabilitation. Pilot studies suggest that such a priming intervention before physiotherapy might increase the responsiveness of the brain to the subsequent physiotherapy, thereby improving the clinical outcome. However, there is little evidence up to now that these BMI-based interventions have achieved operate conditioning of specific brain states that facilitate task-specific functional gains beyond the practice of primed physiotherapy. In this context, we argue that BMI technology needs to aim at physiological features relevant for the targeted behavioral gain. Moreover, this therapeutic intervention has to be informed by concepts of reinforcement learning to develop its full potential. Such a refined neurofeedback approach would need to address the following issues (1 Defining a physiological feedback target specific to the intended behavioral gain, e.g. β-band oscillations for cortico-muscular communication. This targeted brain state could well be different from the brain state optimal for the neurofeedback task (2 Selecting a BMI classification and thresholding approach on the basis of learning principles, i.e. balancing challenge and reward of the neurofeedback task instead of maximizing the classification accuracy of the feedback device (3 Adjusting the feedback in the course of the training period to account for the cognitive load and the learning experience of the participant. The proposed neurofeedback strategy provides evidence for the feasibility of the suggested approach by demonstrating that dynamic threshold adaptation based on reinforcement learning may lead to frequency-specific operant conditioning of β-band oscillations paralleled by task-specific motor improvement; a proposal that requires investigation in a larger cohort of stroke

  11. Smartphone-Based Patients' Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring.

    Science.gov (United States)

    Guo, Junqi; Zhou, Xi; Sun, Yunchuan; Ping, Gong; Zhao, Guoxing; Li, Zhuorong

    2016-06-01

    Smartphone based activity recognition has recently received remarkable attention in various applications of mobile health such as safety monitoring, fitness tracking, and disease prediction. To achieve more accurate and simplified medical monitoring, this paper proposes a self-learning scheme for patients' activity recognition, in which a patient only needs to carry an ordinary smartphone that contains common motion sensors. After the real-time data collection though this smartphone, we preprocess the data using coordinate system transformation to eliminate phone orientation influence. A set of robust and effective features are then extracted from the preprocessed data. Because a patient may inevitably perform various unpredictable activities that have no apriori knowledge in the training dataset, we propose a self-learning activity recognition scheme. The scheme determines whether there are apriori training samples and labeled categories in training pools that well match with unpredictable activity data. If not, it automatically assembles these unpredictable samples into different clusters and gives them new category labels. These clustered samples combined with the acquired new category labels are then merged into the training dataset to reinforce recognition ability of the self-learning model. In experiments, we evaluate our scheme using the data collected from two postoperative patient volunteers, including six labeled daily activities as the initial apriori categories in the training pool. Experimental results demonstrate that the proposed self-learning scheme for activity recognition works very well for most cases. When there exist several types of unseen activities without any apriori information, the accuracy reaches above 80 % after the self-learning process converges.

  12. Enhanced fuzzy-connective-based hierarchical aggregation network using particle swarm optimization

    Science.gov (United States)

    Wang, Fang-Fang; Su, Chao-Ton

    2014-11-01

    The fuzzy-connective-based aggregation network is similar to the human decision-making process. It is capable of aggregating and propagating degrees of satisfaction of a set of criteria in a hierarchical manner. Its interpreting ability and transparency make it especially desirable. To enhance its effectiveness and further applicability, a learning approach is successfully developed based on particle swarm optimization to determine the weights and parameters of the connectives in the network. By experimenting on eight datasets with different characteristics and conducting further statistical tests, it has been found to outperform the gradient- and genetic algorithm-based learning approaches proposed in the literature; furthermore, it is capable of generating more accurate estimates. The present approach retains the original benefits of fuzzy-connective-based aggregation networks and is widely applicable. The characteristics of the learning approaches are also discussed and summarized, providing better understanding of the similarities and differences among these three approaches.

  13. Reinforcement learning for microgrid energy management

    International Nuclear Information System (INIS)

    Kuznetsova, Elizaveta; Li, Yan-Fu; Ruiz, Carlos; Zio, Enrico; Ault, Graham; Bell, Keith

    2013-01-01

    We consider a microgrid for energy distribution, with a local consumer, a renewable generator (wind turbine) and a storage facility (battery), connected to the external grid via a transformer. We propose a 2 steps-ahead reinforcement learning algorithm to plan the battery scheduling, which plays a key role in the achievement of the consumer goals. The underlying framework is one of multi-criteria decision-making by an individual consumer who has the goals of increasing the utilization rate of the battery during high electricity demand (so as to decrease the electricity purchase from the external grid) and increasing the utilization rate of the wind turbine for local use (so as to increase the consumer independence from the external grid). Predictions of available wind power feed the reinforcement learning algorithm for selecting the optimal battery scheduling actions. The embedded learning mechanism allows to enhance the consumer knowledge about the optimal actions for battery scheduling under different time-dependent environmental conditions. The developed framework gives the capability to intelligent consumers to learn the stochastic environment and make use of the experience to select optimal energy management actions. - Highlights: • A consumer exploits a 2 steps-ahead reinforcement learning for battery scheduling. • The Q-learning based mechanism is fed by the predictions of available wind power. • Wind speed state evolutions are modeled with a Markov chain model. • Optimal scheduling actions are learned through the occurrence of similar scenarios. • The consumer manifests a continuous enhance of his knowledge about optimal actions

  14. Surface blemish detection from passive imagery using learned fuzzy set concepts

    International Nuclear Information System (INIS)

    Gurbuz, S.; Carver, A.; Schalkoff, R.

    1997-12-01

    An image analysis method for real-time surface blemish detection using passive imagery and fuzzy set concepts is described. The method develops an internal knowledge representation for surface blemish characteristics on the basis of experience, thus facilitating autonomous learning based upon positive and negative exemplars. The method incorporates fuzzy set concepts in the learning subsystem and image segmentation algorithms, thereby mimicking human visual perception. This enables a generic solution for color image segmentation. This method has been applied in the development of ARIES (Autonomous Robotic Inspection Experimental System), designed to inspect DOE warehouse waste storage drums for rust. In this project, the ARIES vision system is used to acquire drum surface images under controlled conditions and subsequently perform visual inspection leading to the classification of the drum as acceptable or suspect

  15. eFSM--a novel online neural-fuzzy semantic memory model.

    Science.gov (United States)

    Tung, Whye Loon; Quek, Chai

    2010-01-01

    Fuzzy rule-based systems (FRBSs) have been successfully applied to many areas. However, traditional fuzzy systems are often manually crafted, and their rule bases that represent the acquired knowledge are static and cannot be trained to improve the modeling performance. This subsequently leads to intensive research on the autonomous construction and tuning of a fuzzy system directly from the observed training data to address the knowledge acquisition bottleneck, resulting in well-established hybrids such as neural-fuzzy systems (NFSs) and genetic fuzzy systems (GFSs). However, the complex and dynamic nature of real-world problems demands that fuzzy rule-based systems and models be able to adapt their parameters and ultimately evolve their rule bases to address the nonstationary (time-varying) characteristics of their operating environments. Recently, considerable research efforts have been directed to the study of evolving Tagaki-Sugeno (T-S)-type NFSs based on the concept of incremental learning. In contrast, there are very few incremental learning Mamdani-type NFSs reported in the literature. Hence, this paper presents the evolving neural-fuzzy semantic memory (eFSM) model, a neural-fuzzy Mamdani architecture with a data-driven progressively adaptive structure (i.e., rule base) based on incremental learning. Issues related to the incremental learning of the eFSM rule base are carefully investigated, and a novel parameter learning approach is proposed for the tuning of the fuzzy set parameters in eFSM. The proposed eFSM model elicits highly interpretable semantic knowledge in the form of Mamdani-type if-then fuzzy rules from low-level numeric training data. These Mamdani fuzzy rules define the computing structure of eFSM and are incrementally learned with the arrival of each training data sample. New rules are constructed from the emergence of novel training data and obsolete fuzzy rules that no longer describe the recently observed data trends are pruned. This

  16. Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor

    Energy Technology Data Exchange (ETDEWEB)

    Ondrej Linda; Todd Vollmer; Jason Wright; Milos Manic

    2011-04-01

    Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.

  17. Image matching navigation based on fuzzy information

    Institute of Scientific and Technical Information of China (English)

    田玉龙; 吴伟仁; 田金文; 柳健

    2003-01-01

    In conventional image matching methods, the image matching process is mostly based on image statistic information. One aspect neglected by all these methods is that there is much fuzzy information contained in these images. A new fuzzy matching algorithm based on fuzzy similarity for navigation is presented in this paper. Because the fuzzy theory is of the ability of making good description of the fuzzy information contained in images, the image matching method based on fuzzy similarity would look forward to producing good performance results. Experimental results using matching algorithm based on fuzzy information also demonstrate its reliability and practicability.

  18. Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model

    Directory of Open Access Journals (Sweden)

    Bogdan Gliwa

    2011-01-01

    Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.

  19. Fuzzy logic of Aristotelian forms

    Energy Technology Data Exchange (ETDEWEB)

    Perlovsky, L.I. [Nichols Research Corp., Lexington, MA (United States)

    1996-12-31

    Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties. In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial complexity to linear one. I will discuss the relationship of the new computational paradigm to two theories due to Aristotle: theory of Forms and logic. While theory of Forms argued that the mind cannot be based on ready-made a priori concepts, Aristotelian logic operated with just such concepts. I discuss an interpretation of MFT suggesting that its fuzzy logic, combining a-priority and adaptivity, implements Aristotelian theory of Forms (theory of mind). Thus, 2300 years after Aristotle, a logic is developed suitable for his theory of mind.

  20. TEACHING SELF-CONTROL WITH QUALITATIVELY DIFFERENT REINFORCERS

    OpenAIRE

    Passage, Michael; Tincani, Matt; Hantula, Donald A.

    2012-01-01

    This study examined the effectiveness of using qualitatively different reinforcers to teach self-control to an adolescent boy who had been diagnosed with an intellectual disability. First, he was instructed to engage in an activity without programmed reinforcement. Next, he was instructed to engage in the activity under a two-choice fixed-duration schedule of reinforcement. Finally, he was exposed to self-control training, during which the delay to a more preferred reinforcer was initially sh...

  1. From Creatures of Habit to Goal-Directed Learners: Tracking the Developmental Emergence of Model-Based Reinforcement Learning.

    Science.gov (United States)

    Decker, Johannes H; Otto, A Ross; Daw, Nathaniel D; Hartley, Catherine A

    2016-06-01

    Theoretical models distinguish two decision-making strategies that have been formalized in reinforcement-learning theory. A model-based strategy leverages a cognitive model of potential actions and their consequences to make goal-directed choices, whereas a model-free strategy evaluates actions based solely on their reward history. Research in adults has begun to elucidate the psychological mechanisms and neural substrates underlying these learning processes and factors that influence their relative recruitment. However, the developmental trajectory of these evaluative strategies has not been well characterized. In this study, children, adolescents, and adults performed a sequential reinforcement-learning task that enabled estimation of model-based and model-free contributions to choice. Whereas a model-free strategy was apparent in choice behavior across all age groups, a model-based strategy was absent in children, became evident in adolescents, and strengthened in adults. These results suggest that recruitment of model-based valuation systems represents a critical cognitive component underlying the gradual maturation of goal-directed behavior. © The Author(s) 2016.

  2. 5th International Conference on Fuzzy and Neuro Computing

    CERN Document Server

    Panigrahi, Bijaya; Das, Swagatam; Suganthan, Ponnuthurai

    2015-01-01

    This proceedings bring together contributions from researchers from academia and industry to report the latest cutting edge research made in the areas of Fuzzy Computing, Neuro Computing and hybrid Neuro-Fuzzy Computing in the paradigm of Soft Computing. The FANCCO 2015 conference explored new application areas, design novel hybrid algorithms for solving different real world application problems. After a rigorous review of the 68 submissions from all over the world, the referees panel selected 27 papers to be presented at the Conference. The accepted papers have a good, balanced mix of theory and applications. The techniques ranged from fuzzy neural networks, decision trees, spiking neural networks, self organizing feature map, support vector regression, adaptive neuro fuzzy inference system, extreme learning machine, fuzzy multi criteria decision making, machine learning, web usage mining, Takagi-Sugeno Inference system, extended Kalman filter, Goedel type logic, fuzzy formal concept analysis, biclustering e...

  3. Reinforcement Learning Based Web Service Compositions for Mobile Business

    Science.gov (United States)

    Zhou, Juan; Chen, Shouming

    In this paper, we propose a new solution to Reactive Web Service Composition, via molding with Reinforcement Learning, and introducing modified (alterable) QoS variables into the model as elements in the Markov Decision Process tuple. Moreover, we give an example of Reactive-WSC-based mobile banking, to demonstrate the intrinsic capability of the solution in question of obtaining the optimized service composition, characterized by (alterable) target QoS variable sets with optimized values. Consequently, we come to the conclusion that the solution has decent potentials in boosting customer experiences and qualities of services in Web Services, and those in applications in the whole electronic commerce and business sector.

  4. Immune Genetic Learning of Fuzzy Cognitive Map

    Institute of Scientific and Technical Information of China (English)

    LIN Chun-mei; HE Yue; TANG Bing-yong

    2006-01-01

    This paper presents a hybrid methodology of automatically constructing fuzzy cognitive map (FCM). The method uses immune genetic algorithm to learn the connection matrix of FCM. In the algorithm, the DNA coding method is used and an immune operator based on immune mechanism is constructed. The characteristics of the system and the experts' knowledge are abstracted as vaccine for restraining the degenerative phenomena during evolution so as to improve the algorithmic efficiency. Finally, an illustrative example is provided, and its results suggest that the method is capable of automatically generating FCM model.

  5. Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning

    Directory of Open Access Journals (Sweden)

    Ya’nan Wang

    2016-01-01

    Full Text Available Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.

  6. ANALYSIS OF FUZZY QUEUES: PARAMETRIC PROGRAMMING APPROACH BASED ON RANDOMNESS - FUZZINESS CONSISTENCY PRINCIPLE

    Directory of Open Access Journals (Sweden)

    Dhruba Das

    2015-04-01

    Full Text Available In this article, based on Zadeh’s extension principle we have apply the parametric programming approach to construct the membership functions of the performance measures when the interarrival time and the service time are fuzzy numbers based on the Baruah’s Randomness- Fuzziness Consistency Principle. The Randomness-Fuzziness Consistency Principle leads to defining a normal law of fuzziness using two different laws of randomness. In this article, two fuzzy queues FM/M/1 and M/FM/1 has been studied and constructed their membership functions of the system characteristics based on the aforesaid principle. The former represents a queue with fuzzy exponential arrivals and exponential service rate while the latter represents a queue with exponential arrival rate and fuzzy exponential service rate.

  7. Identification of Fuzzy Inference Systems by Means of a Multiobjective Opposition-Based Space Search Algorithm

    Directory of Open Access Journals (Sweden)

    Wei Huang

    2013-01-01

    Full Text Available We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA. The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effectiveness of the proposed fuzzy models.

  8. Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.

    Science.gov (United States)

    Liu, Yu-Ting; Lin, Yang-Yin; Wu, Shang-Lin; Chuang, Chun-Hsiang; Lin, Chin-Teng

    2016-02-01

    This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.

  9. Design of a self-adaptive fuzzy PID controller for piezoelectric ceramics micro-displacement system

    Science.gov (United States)

    Zhang, Shuang; Zhong, Yuning; Xu, Zhongbao

    2008-12-01

    In order to improve control precision of the piezoelectric ceramics (PZT) micro-displacement system, a self-adaptive fuzzy Proportional Integration Differential (PID) controller is designed based on the traditional digital PID controller combining with fuzzy control. The arithmetic gives a fuzzy control rule table with the fuzzy control rule and fuzzy reasoning, through this table, the PID parameters can be adjusted online in real time control. Furthermore, the automatic selective control is achieved according to the change of the error. The controller combines the good dynamic capability of the fuzzy control and the high stable precision of the PID control, adopts the method of using fuzzy control and PID control in different segments of time. In the initial and middle stage of the transition process of system, that is, when the error is larger than the value, fuzzy control is used to adjust control variable. It makes full use of the fast response of the fuzzy control. And when the error is smaller than the value, the system is about to be in the steady state, PID control is adopted to eliminate static error. The problems of PZT existing in the field of precise positioning are overcome. The results of the experiments prove that the project is correct and practicable.

  10. ANALYSIS OF FUZZY QUEUES: PARAMETRIC PROGRAMMING APPROACH BASED ON RANDOMNESS - FUZZINESS CONSISTENCY PRINCIPLE

    OpenAIRE

    Dhruba Das; Hemanta K. Baruah

    2015-01-01

    In this article, based on Zadeh’s extension principle we have apply the parametric programming approach to construct the membership functions of the performance measures when the interarrival time and the service time are fuzzy numbers based on the Baruah’s Randomness- Fuzziness Consistency Principle. The Randomness-Fuzziness Consistency Principle leads to defining a normal law of fuzziness using two different laws of randomness. In this article, two fuzzy queues FM...

  11. A new approach to self-organizing fuzzy polynomial neural networks guided by genetic optimization

    International Nuclear Information System (INIS)

    Oh, Sung-Kwun; Pedrycz, Witold

    2005-01-01

    In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology. The underlying methodology involves mechanisms of genetic optimization, especially genetic algorithms (GAs). Let us recall that the design of the 'conventional' FPNNs uses an extended Group Method of Data Handling (GMDH) and exploits a fixed fuzzy inference type located at each FPN of the FPNN as well as considers a fixed number of input nodes at FPNs (or nodes) located in each layer. The proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. The performance of the proposed gFPNN is quantified through experimentation that exploits standard data already being used in fuzzy modeling. The results reveal superiority of the proposed networks over the existing fuzzy and neural models

  12. Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling

    Directory of Open Access Journals (Sweden)

    Nebot

    2012-04-01

    Full Text Available In this research a genetic fuzzy system (GFS is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR methodology and the Linguistic Rule FIR (LR-FIR algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR models and decision support (LR-FIR models. The GFS is evaluated in an e-learning context.

  13. Self-Play and Using an Expert to Learn to Play Backgammon with Temporal Difference Learning

    NARCIS (Netherlands)

    Wiering, Marco A.

    2010-01-01

    A promising approach to learn to play board games is to use reinforcement learning algorithms that can learn a game position evaluation function. In this paper we examine and compare three different methods for generating training games: 1) Learning by self-play, 2) Learning by playing against an

  14. Enriching behavioral ecology with reinforcement learning methods.

    Science.gov (United States)

    Frankenhuis, Willem E; Panchanathan, Karthik; Barto, Andrew G

    2018-02-13

    This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  15. Enhancing Self-Efficacy in Elementary Science Teaching With Professional Learning Communities

    Science.gov (United States)

    Mintzes, Joel J.; Marcum, Bev; Messerschmidt-Yates, Christl; Mark, Andrew

    2013-11-01

    Emerging from Bandura's Social Learning Theory, this study of in-service elementary school teachers examined the effects of sustained Professional Learning Communities (PLCs) on self-efficacy in science teaching. Based on mixed research methods, and a non-equivalent control group experimental design, the investigation explored changes in personal self-efficacy and outcome expectancy among teachers engaged in PLCs that featured Demonstration Laboratories, Lesson Study, and annual Summer Institutes. Significant changes favoring the experimental group were found on all quantitative measures of self-efficacy. Structured clinical interviews revealed that observed changes were largely attributable to a wide range of direct (mastery) and vicarious experiences, as well as emotional reinforcement and social persuasion.

  16. Neuro-fuzzy system modeling based on automatic fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    Yuangang TANG; Fuchun SUN; Zengqi SUN

    2005-01-01

    A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.

  17. Fuzzy Constraint-Based Agent Negotiation

    Institute of Scientific and Technical Information of China (English)

    Menq-Wen Lin; K. Robert Lai; Ting-Jung Yu

    2005-01-01

    Conflicts between two or more parties arise for various reasons and perspectives. Thus, resolution of conflicts frequently relies on some form of negotiation. This paper presents a general problem-solving framework for modeling multi-issue multilateral negotiation using fuzzy constraints. Agent negotiation is formulated as a distributed fuzzy constraint satisfaction problem (DFCSP). Fuzzy constrains are thus used to naturally represent each agent's desires involving imprecision and human conceptualization, particularly when lexical imprecision and subjective matters are concerned. On the other hand, based on fuzzy constraint-based problem-solving, our approach enables an agent not only to systematically relax fuzzy constraints to generate a proposal, but also to employ fuzzy similarity to select the alternative that is subject to its acceptability by the opponents. This task of problem-solving is to reach an agreement that benefits all agents with a high satisfaction degree of fuzzy constraints, and move towards the deal more quickly since their search focuses only on the feasible solution space. An application to multilateral negotiation of a travel planning is provided to demonstrate the usefulness and effectiveness of our framework.

  18. Medical students' self-efficacy in problem-based learning and its relationship with self-regulated learning.

    Science.gov (United States)

    Demirören, Meral; Turan, Sevgi; Öztuna, Derya

    2016-01-01

    Problem-based learning (PBL) is most commonly used in medical education to enhance self-regulated learning (SRL) skills. Self-efficacy beliefs affect students' motivation through self-regulatory processes. The purpose of this study is to examine the relationship between medical students' self-reported SRL skills and their self-efficacy in PBL. A cross-sectional study was conducted with second (286; 83.1%) and third (275; 80.2%) year students at the Ankara University School of Medicine. The SRL perception (SRLP) scale and self-efficacy for problem-based learning (SPBL) scale were used in the study. The SRLP subscales were positively correlated with the SPBL subscales. There was a weak but meaningful correlation between the subscales of SRLP (with the exception of the lack of self-directedness scale) and the subscales of SPBL and the students' views on benefiting from PBL. The female students' mean score was higher for the 'planning and goal setting' subscale of SRLP (p=0.017), and the second-year students' mean score was higher than that of the third-year students for the 'lack of self-directedness' subscale of SRLP (p=0.001) with small effect sizes (Cohen's d is 0.17 and 0.27). There was no statistically significant difference between the year and subscales of SPBL. With regard to gender, the female students had higher scores than the male students on the 'responsibility' subscale of SPBL (p=0.003; Cohen's d=0.26). The study showed that medical students used SRL skills and believed in their ability to learn effectively in the PBL context and demonstrated the relationship between SRL skills and self-efficacy beliefs. Monitoring students' development in these skills and giving them feedback could be beneficial for the cognitive achievement of students with learning difficulties and insufficient study skills. Further studies need to be undertaken to investigate issues such as the curriculum, learning environment, individual differences, and how these can affect the

  19. Medical students’ self-efficacy in problem-based learning and its relationship with self-regulated learning

    Science.gov (United States)

    Demirören, Meral; Turan, Sevgi; Öztuna, Derya

    2016-01-01

    Background Problem-based learning (PBL) is most commonly used in medical education to enhance self-regulated learning (SRL) skills. Self-efficacy beliefs affect students’ motivation through self-regulatory processes. The purpose of this study is to examine the relationship between medical students’ self-reported SRL skills and their self-efficacy in PBL. Methods A cross-sectional study was conducted with second (286; 83.1%) and third (275; 80.2%) year students at the Ankara University School of Medicine. The SRL perception (SRLP) scale and self-efficacy for problem-based learning (SPBL) scale were used in the study. Results The SRLP subscales were positively correlated with the SPBL subscales. There was a weak but meaningful correlation between the subscales of SRLP (with the exception of the lack of self-directedness scale) and the subscales of SPBL and the students’ views on benefiting from PBL. The female students’ mean score was higher for the ‘planning and goal setting’ subscale of SRLP (p=0.017), and the second-year students’ mean score was higher than that of the third-year students for the ‘lack of self-directedness’ subscale of SRLP (p=0.001) with small effect sizes (Cohen's d is 0.17 and 0.27). There was no statistically significant difference between the year and subscales of SPBL. With regard to gender, the female students had higher scores than the male students on the ‘responsibility’ subscale of SPBL (p=0.003; Cohen's d=0.26). Conclusions The study showed that medical students used SRL skills and believed in their ability to learn effectively in the PBL context and demonstrated the relationship between SRL skills and self-efficacy beliefs. Monitoring students’ development in these skills and giving them feedback could be beneficial for the cognitive achievement of students with learning difficulties and insufficient study skills. Further studies need to be undertaken to investigate issues such as the curriculum, learning

  20. Medical students’ self-efficacy in problem-based learning and its relationship with self-regulated learning

    Directory of Open Access Journals (Sweden)

    Meral Demirören

    2016-03-01

    Full Text Available Background: Problem-based learning (PBL is most commonly used in medical education to enhance self-regulated learning (SRL skills. Self-efficacy beliefs affect students’ motivation through self-regulatory processes. The purpose of this study is to examine the relationship between medical students’ self-reported SRL skills and their self-efficacy in PBL. Methods: A cross-sectional study was conducted with second (286; 83.1% and third (275; 80.2% year students at the Ankara University School of Medicine. The SRL perception (SRLP scale and self-efficacy for problem-based learning (SPBL scale were used in the study. Results: The SRLP subscales were positively correlated with the SPBL subscales. There was a weak but meaningful correlation between the subscales of SRLP (with the exception of the lack of self-directedness scale and the subscales of SPBL and the students’ views on benefiting from PBL. The female students’ mean score was higher for the ‘planning and goal setting’ subscale of SRLP (p=0.017, and the second-year students’ mean score was higher than that of the third-year students for the ‘lack of self-directedness’ subscale of SRLP (p=0.001 with small effect sizes (Cohen's d is 0.17 and 0.27. There was no statistically significant difference between the year and subscales of SPBL. With regard to gender, the female students had higher scores than the male students on the ‘responsibility’ subscale of SPBL (p=0.003; Cohen's d=0.26. Conclusions: The study showed that medical students used SRL skills and believed in their ability to learn effectively in the PBL context and demonstrated the relationship between SRL skills and self-efficacy beliefs. Monitoring students’ development in these skills and giving them feedback could be beneficial for the cognitive achievement of students with learning difficulties and insufficient study skills. Further studies need to be undertaken to investigate issues such as the curriculum

  1. Introduction to type-2 fuzzy logic control theory and applications

    CERN Document Server

    Mendel, Jerry M; Tan, Woei-Wan; Melek, William W; Ying, Hao

    2014-01-01

    Written by world-class leaders in type-2 fuzzy logic control, this book offers a self-contained reference for both researchers and students. The coverage provides both background and an extensive literature survey on fuzzy logic and related type-2 fuzzy control. It also includes research questions, experiment and simulation results, and downloadable computer programs on an associated website. This key resource will prove useful to students and engineers wanting to learn type-2 fuzzy control theory and its applications.

  2. Reinforcement Learning for Ramp Control: An Analysis of Learning Parameters

    Directory of Open Access Journals (Sweden)

    Chao Lu

    2016-08-01

    Full Text Available Reinforcement Learning (RL has been proposed to deal with ramp control problems under dynamic traffic conditions; however, there is a lack of sufficient research on the behaviour and impacts of different learning parameters. This paper describes a ramp control agent based on the RL mechanism and thoroughly analyzed the influence of three learning parameters; namely, learning rate, discount rate and action selection parameter on the algorithm performance. Two indices for the learning speed and convergence stability were used to measure the algorithm performance, based on which a series of simulation-based experiments were designed and conducted by using a macroscopic traffic flow model. Simulation results showed that, compared with the discount rate, the learning rate and action selection parameter made more remarkable impacts on the algorithm performance. Based on the analysis, some suggestionsabout how to select suitable parameter values that can achieve a superior performance were provided.

  3. Separation of time-based and trial-based accounts of the partial reinforcement extinction effect.

    Science.gov (United States)

    Bouton, Mark E; Woods, Amanda M; Todd, Travis P

    2014-01-01

    Two appetitive conditioning experiments with rats examined time-based and trial-based accounts of the partial reinforcement extinction effect (PREE). In the PREE, the loss of responding that occurs in extinction is slower when the conditioned stimulus (CS) has been paired with a reinforcer on some of its presentations (partially reinforced) instead of every presentation (continuously reinforced). According to a time-based or "time-accumulation" view (e.g., Gallistel and Gibbon, 2000), the PREE occurs because the organism has learned in partial reinforcement to expect the reinforcer after a larger amount of time has accumulated in the CS over trials. In contrast, according to a trial-based view (e.g., Capaldi, 1967), the PREE occurs because the organism has learned in partial reinforcement to expect the reinforcer after a larger number of CS presentations. Experiment 1 used a procedure that equated partially and continuously reinforced groups on their expected times to reinforcement during conditioning. A PREE was still observed. Experiment 2 then used an extinction procedure that allowed time in the CS and the number of trials to accumulate differentially through extinction. The PREE was still evident when responding was examined as a function of expected time units to the reinforcer, but was eliminated when responding was examined as a function of expected trial units to the reinforcer. There was no evidence that the animal responded according to the ratio of time accumulated during the CS in extinction over the time in the CS expected before the reinforcer. The results thus favor a trial-based account over a time-based account of extinction and the PREE. This article is part of a Special Issue entitled: Associative and Temporal Learning. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Self-directed learning readiness of Asian students: students perspective on a hybrid problem based learning curriculum.

    Science.gov (United States)

    Leatemia, Lukas D; Susilo, Astrid P; van Berkel, Henk

    2016-12-03

    To identify the student's readiness to perform self-directed learning and the underlying factors influencing it on the hybrid problem based learning curriculum. A combination of quantitative and qualitative studies was conducted in five medical schools in Indonesia. In the quantitative study, the Self Directed Learning Readiness Scale was distributed to all students in all batches, who had experience with the hybrid problem based curriculum. They were categorized into low- and high -level based on the score of the questionnaire. Three focus group discussions (low-, high-, and mixed level) were conducted in the qualitative study with six to twelve students chosen randomly from each group to find the factors influencing their self-directed learning readiness. Two researchers analysed the qualitative data as a measure of triangulation. The quantitative study showed only half of the students had a high-level of self-directed learning readiness, and a similar trend also occurred in each batch. The proportion of students with a high level of self-directed learning readiness was lower in the senior students compared to more junior students. The qualitative study showed that problem based learning processes, assessments, learning environment, students' life styles, students' perceptions of the topics, and mood, were factors influencing their self-directed learning. A hybrid problem based curriculum may not fully affect the students' self-directed learning. The curriculum system, teacher's experiences, student's background and cultural factors might contribute to the difficulties for the student's in conducting self-directed learning.

  5. Fuzzy Control Tutorial

    DEFF Research Database (Denmark)

    Dotoli, M.; Jantzen, Jan

    1999-01-01

    The tutorial concerns automatic control of an inverted pendulum, especially rule based control by means of fuzzy logic. A ball balancer, implemented in a software simulator in Matlab, is used as a practical case study. The objectives of the tutorial are to teach the basics of fuzzy control......, and to show how to apply fuzzy logic in automatic control. The tutorial is distance learning, where students interact one-to-one with the teacher using e-mail....

  6. Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Syed Zulqadar Hassan

    2017-03-01

    Full Text Available An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.

  7. Fuzzy-based HAZOP study for process industry

    Energy Technology Data Exchange (ETDEWEB)

    Ahn, Junkeon; Chang, Daejun, E-mail: djchang@kaist.edu

    2016-11-05

    Highlights: • HAZOP is the important technique to evaluate system safety and its risks while process operations. • Fuzzy theory can handle the inherent uncertainties of process systems for the HAZOP. • Fuzzy-based HAZOP considers the aleatory and epistemic uncertainties and provides the risk level with less uncertainty. • Risk acceptance criteria should be considered regarding the transition region for each risk. - Abstract: This study proposed a fuzzy-based HAZOP for analyzing process hazards. Fuzzy theory was used to express uncertain states. This theory was found to be a useful approach to overcome the inherent uncertainty in HAZOP analyses. Fuzzy logic sharply contrasted with classical logic and provided diverse risk values according to its membership degree. Appropriate process parameters and guidewords were selected to describe the frequency and consequence of an accident. Fuzzy modeling calculated risks based on the relationship between the variables of an accident. The modeling was based on the mean expected value, trapezoidal fuzzy number, IF-THEN rules, and the center of gravity method. A cryogenic LNG (liquefied natural gas) testing facility was the objective process for the fuzzy-based and conventional HAZOPs. The most significant index is the frequency to determine risks. The comparison results showed that the fuzzy-based HAZOP provides better sophisticated risks than the conventional HAZOP. The fuzzy risk matrix presents the significance of risks, negligible risks, and necessity of risk reduction.

  8. Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

    Science.gov (United States)

    Akhavan, P.; Karimi, M.; Pahlavani, P.

    2014-10-01

    Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

  9. Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    P. Akhavan

    2014-10-01

    Full Text Available Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

  10. Learning to reach by reinforcement learning using a receptive field based function approximation approach with continuous actions.

    Science.gov (United States)

    Tamosiunaite, Minija; Asfour, Tamim; Wörgötter, Florentin

    2009-03-01

    Reinforcement learning methods can be used in robotics applications especially for specific target-oriented problems, for example the reward-based recalibration of goal directed actions. To this end still relatively large and continuous state-action spaces need to be efficiently handled. The goal of this paper is, thus, to develop a novel, rather simple method which uses reinforcement learning with function approximation in conjunction with different reward-strategies for solving such problems. For the testing of our method, we use a four degree-of-freedom reaching problem in 3D-space simulated by a two-joint robot arm system with two DOF each. Function approximation is based on 4D, overlapping kernels (receptive fields) and the state-action space contains about 10,000 of these. Different types of reward structures are being compared, for example, reward-on- touching-only against reward-on-approach. Furthermore, forbidden joint configurations are punished. A continuous action space is used. In spite of a rather large number of states and the continuous action space these reward/punishment strategies allow the system to find a good solution usually within about 20 trials. The efficiency of our method demonstrated in this test scenario suggests that it might be possible to use it on a real robot for problems where mixed rewards can be defined in situations where other types of learning might be difficult.

  11. Instructional control of reinforcement learning: a behavioral and neurocomputational investigation.

    Science.gov (United States)

    Doll, Bradley B; Jacobs, W Jake; Sanfey, Alan G; Frank, Michael J

    2009-11-24

    Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S. (Ed.). 1989. Rule-governed behavior: cognition, contingencies, and instructional control. Plenum Press.). Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability. Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction-driven and reinforcement-driven behavior via two potential neural circuits: one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC), and another in which the striatum learns the environmentally based reinforcement contingencies, but is "overridden" at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract "Q-learning" and Bayesian models to subject data. The best-fitting model supports one of the neural models, suggesting the existence of a "confirmation bias" in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes.

  12. A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system.

    Science.gov (United States)

    Lin, Chin-Teng; Wu, Rui-Cheng; Chang, Jyh-Yeong; Liang, Sheng-Fu

    2004-02-01

    In this paper, a new technique for the Chinese text-to-speech (TTS) system is proposed. Our major effort focuses on the prosodic information generation. New methodologies for constructing fuzzy rules in a prosodic model simulating human's pronouncing rules are developed. The proposed Recurrent Fuzzy Neural Network (RFNN) is a multilayer recurrent neural network (RNN) which integrates a Self-cOnstructing Neural Fuzzy Inference Network (SONFIN) into a recurrent connectionist structure. The RFNN can be functionally divided into two parts. The first part adopts the SONFIN as a prosodic model to explore the relationship between high-level linguistic features and prosodic information based on fuzzy inference rules. As compared to conventional neural networks, the SONFIN can always construct itself with an economic network size in high learning speed. The second part employs a five-layer network to generate all prosodic parameters by directly using the prosodic fuzzy rules inferred from the first part as well as other important features of syllables. The TTS system combined with the proposed method can behave not only sandhi rules but also the other prosodic phenomena existing in the traditional TTS systems. Moreover, the proposed scheme can even find out some new rules about prosodic phrase structure. The performance of the proposed RFNN-based prosodic model is verified by imbedding it into a Chinese TTS system with a Chinese monosyllable database based on the time-domain pitch synchronous overlap add (TD-PSOLA) method. Our experimental results show that the proposed RFNN can generate proper prosodic parameters including pitch means, pitch shapes, maximum energy levels, syllable duration, and pause duration. Some synthetic sounds are online available for demonstration.

  13. Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments

    Science.gov (United States)

    Sabourin, Jennifer L.; Shores, Lucy R.; Mott, Bradford W.; Lester, James C.

    2013-01-01

    Self-regulated learning behaviors such as goal setting and monitoring have been found to be crucial to students' success in computer-based learning environments. Consequently, understanding students' self-regulated learning behavior has been the subject of increasing attention. Unfortunately, monitoring these behaviors in real-time has…

  14. A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis.

    Science.gov (United States)

    El-Sappagh, Shaker; Elmogy, Mohammed; Riad, A M

    2015-11-01

    Case-based reasoning (CBR) is a problem-solving paradigm that uses past knowledge to interpret or solve new problems. It is suitable for experience-based and theory-less problems. Building a semantically intelligent CBR that mimic the expert thinking can solve many problems especially medical ones. Knowledge-intensive CBR using formal ontologies is an evolvement of this paradigm. Ontologies can be used for case representation and storage, and it can be used as a background knowledge. Using standard medical ontologies, such as SNOMED CT, enhances the interoperability and integration with the health care systems. Moreover, utilizing vague or imprecise knowledge further improves the CBR semantic effectiveness. This paper proposes a fuzzy ontology-based CBR framework. It proposes a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types. This framework is implemented and tested on the diabetes diagnosis problem. The fuzzy ontology is populated with 60 real diabetic cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies. The resulting system can answer complex medical queries related to semantic understanding of medical concepts and handling of vague terms. The resulting fuzzy case-base ontology has 63 concepts, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy datatypes, and 2640 instances. The system achieves an accuracy of 97.67%. We compare our framework with existing CBR systems and a set of five machine-learning classifiers; our system outperforms all of these systems. Building an integrated CBR system can improve its performance. Representing CBR knowledge using the fuzzy ontology and building a case retrieval algorithm that treats different features differently improves the accuracy of the resulting systems. Copyright © 2015 Elsevier B.V. All rights reserved.

  15. Human-level control through deep reinforcement learning

    Science.gov (United States)

    Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

    2015-02-01

    The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

  16. Human-level control through deep reinforcement learning.

    Science.gov (United States)

    Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

    2015-02-26

    The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

  17. Reinforcement learning signals in the human striatum distinguish learners from nonlearners during reward-based decision making.

    Science.gov (United States)

    Schönberg, Tom; Daw, Nathaniel D; Joel, Daphna; O'Doherty, John P

    2007-11-21

    The computational framework of reinforcement learning has been used to forward our understanding of the neural mechanisms underlying reward learning and decision-making behavior. It is known that humans vary widely in their performance in decision-making tasks. Here, we used a simple four-armed bandit task in which subjects are almost evenly split into two groups on the basis of their performance: those who do learn to favor choice of the optimal action and those who do not. Using models of reinforcement learning we sought to determine the neural basis of these intrinsic differences in performance by scanning both groups with functional magnetic resonance imaging. We scanned 29 subjects while they performed the reward-based decision-making task. Our results suggest that these two groups differ markedly in the degree to which reinforcement learning signals in the striatum are engaged during task performance. While the learners showed robust prediction error signals in both the ventral and dorsal striatum during learning, the nonlearner group showed a marked absence of such signals. Moreover, the magnitude of prediction error signals in a region of dorsal striatum correlated significantly with a measure of behavioral performance across all subjects. These findings support a crucial role of prediction error signals, likely originating from dopaminergic midbrain neurons, in enabling learning of action selection preferences on the basis of obtained rewards. Thus, spontaneously observed individual differences in decision making performance demonstrate the suggested dependence of this type of learning on the functional integrity of the dopaminergic striatal system in humans.

  18. Scaffolding in geometry based on self regulated learning

    Science.gov (United States)

    Bayuningsih, A. S.; Usodo, B.; Subanti, S.

    2017-12-01

    This research aim to know the influence of problem based learning model by scaffolding technique on junior high school student’s learning achievement. This research took location on the junior high school in Banyumas. The research data obtained through mathematic learning achievement test and self-regulated learning (SRL) questioner. Then, the data analysis used two ways ANOVA. The results showed that scaffolding has positive effect to the mathematic learning achievement. The mathematic learning achievement use PBL-Scaffolding model is better than use PBL. The high SRL category student has better mathematic learning achievement than middle and low SRL categories, and then the middle SRL category has better than low SRL category. So, there are interactions between learning model with self-regulated learning in increasing mathematic learning achievement.

  19. Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Yue Hu

    2018-01-01

    Full Text Available An energy management strategy (EMS is important for hybrid electric vehicles (HEVs since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions online. Therefore, it is useful that the EMS can learn from the environment or driving cycle. In this paper, a deep reinforcement learning (DRL-based EMS is designed such that it can learn to select actions directly from the states without any prediction or predefined rules. Furthermore, a DRL-based online learning architecture is presented. It is significant for applying the DRL algorithm in HEV energy management under different driving conditions. Simulation experiments have been conducted using MATLAB and Advanced Vehicle Simulator (ADVISOR co-simulation. Experimental results validate the effectiveness of the DRL-based EMS compared with the rule-based EMS in terms of fuel economy. The online learning architecture is also proved to be effective. The proposed method ensures the optimality, as well as real-time applicability, in HEVs.

  20. A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice

    OpenAIRE

    Bathellier, Brice; Tee, Sui Poh; Hrovat, Christina; Rumpel, Simon

    2013-01-01

    Learning speed can strongly differ across individuals. This is seen in humans and animals. Here, we measured learning speed in mice performing a discrimination task and developed a theoretical model based on the reinforcement learning framework to account for differences between individual mice. We found that, when using a multiplicative learning rule, the starting connectivity values of the model strongly determine the shape of learning curves. This is in contrast to current learning models ...

  1. Framework for robot skill learning using reinforcement learning

    Science.gov (United States)

    Wei, Yingzi; Zhao, Mingyang

    2003-09-01

    Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is an on-line actor critic method for a robot to develop its skill. The reinforcement function has become the critical component for its effect of evaluating the action and guiding the learning process. We present an augmented reward function that provides a new way for RL controller to incorporate prior knowledge and experience into the RL controller. Also, the difference form of augmented reward function is considered carefully. The additional reward beyond conventional reward will provide more heuristic information for RL. In this paper, we present a strategy for the task of complex skill learning. Automatic robot shaping policy is to dissolve the complex skill into a hierarchical learning process. The new form of value function is introduced to attain smooth motion switching swiftly. We present a formal, but practical, framework for robot skill learning and also illustrate with an example the utility of method for learning skilled robot control on line.

  2. Abrasive slurry jet cutting model based on fuzzy relations

    Science.gov (United States)

    Qiang, C. H.; Guo, C. W.

    2017-12-01

    The cutting process of pre-mixed abrasive slurry or suspension jet (ASJ) is a complex process affected by many factors, and there is a highly nonlinear relationship between the cutting parameters and cutting quality. In this paper, guided by fuzzy theory, the fuzzy cutting model of ASJ was developed. In the modeling of surface roughness, the upper surface roughness prediction model and the lower surface roughness prediction model were established respectively. The adaptive fuzzy inference system combines the learning mechanism of neural networks and the linguistic reasoning ability of the fuzzy system, membership functions, and fuzzy rules are obtained by adaptive adjustment. Therefore, the modeling process is fast and effective. In this paper, the ANFIS module of MATLAB fuzzy logic toolbox was used to establish the fuzzy cutting model of ASJ, which is found to be quite instrumental to ASJ cutting applications.

  3. The Effects of ePortfolio-Based Learning Model on Student Self-Regulated Learning

    Science.gov (United States)

    Nguyen, Lap Trung; Ikeda, Mitsuru

    2015-01-01

    Self-regulated learners are aware of their knowledge and skills and proactive in learning. They view learning as a controllable process and accept more responsibility for the results of this process. The research described in this article proposes, implements, and evaluates an ePortfolio-based self-regulated learning model. An ePortfolio system…

  4. A Fuzzy Knowledge Representation Model for Student Performance Assessment

    DEFF Research Database (Denmark)

    Badie, Farshad

    Knowledge representation models based on Fuzzy Description Logics (DLs) can provide a foundation for reasoning in intelligent learning environments. While basic DLs are suitable for expressing crisp concepts and binary relationships, Fuzzy DLs are capable of processing degrees of truth/completene......Knowledge representation models based on Fuzzy Description Logics (DLs) can provide a foundation for reasoning in intelligent learning environments. While basic DLs are suitable for expressing crisp concepts and binary relationships, Fuzzy DLs are capable of processing degrees of truth....../completeness about vague or imprecise information. This paper tackles the issue of representing fuzzy classes using OWL2 in a dataset describing Performance Assessment Results of Students (PARS)....

  5. Fuzzy linear programming based optimal fuel scheduling incorporating blending/transloading facilities

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.; Babic, B.; Milosevic, B. [Electrical Engineering Inst. Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D.J. [EPRI, Palo Alto, CA (United States). Power System Control; Pao, Y.H. [Case Western Reserve Univ., Cleveland, OH (United States)]|[AI WARE, Inc., Cleveland, OH (United States)

    1996-05-01

    In this paper the blending/transloading facilities are modeled using an interactive fuzzy linear programming (FLP), in order to allow the decision-maker to solve the problem of uncertainty of input information within the fuel scheduling optimization. An interactive decision-making process is formulated in which decision-maker can learn to recognize good solutions by considering all possibilities of fuzziness. The application of the fuzzy formulation is accompanied by a careful examination of the definition of fuzziness, appropriateness of the membership function and interpretation of results. The proposed concept provides a decision support system with integration-oriented features, whereby the decision-maker can learn to recognize the relative importance of factors in the specific domain of optimal fuel scheduling (OFS) problem. The formulation of a fuzzy linear programming problem to obtain a reasonable nonfuzzy solution under consideration of the ambiguity of parameters, represented by fuzzy numbers, is introduced. An additional advantage of the FLP formulation is its ability to deal with multi-objective problems.

  6. A new learning algorithm for a fully connected neuro-fuzzy inference system.

    Science.gov (United States)

    Chen, C L Philip; Wang, Jing; Wang, Chi-Hsu; Chen, Long

    2014-10-01

    A traditional neuro-fuzzy system is transformed into an equivalent fully connected three layer neural network (NN), namely, the fully connected neuro-fuzzy inference systems (F-CONFIS). The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind of multilayer NN. Therefore, an efficient learning algorithm for the F-CONFIS to cope these repeated weights is derived. Furthermore, a dynamic learning rate is proposed for neuro-fuzzy systems via F-CONFIS where both premise (hidden) and consequent portions are considered. Several simulation results indicate that the proposed approach achieves much better accuracy and fast convergence.

  7. Adaptive inferential sensors based on evolving fuzzy models.

    Science.gov (United States)

    Angelov, Plamen; Kordon, Arthur

    2010-04-01

    A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can address the

  8. Reinforcement learning in complementarity game and population dynamics.

    Science.gov (United States)

    Jost, Jürgen; Li, Wei

    2014-02-01

    We systematically test and compare different reinforcement learning schemes in a complementarity game [J. Jost and W. Li, Physica A 345, 245 (2005)] played between members of two populations. More precisely, we study the Roth-Erev, Bush-Mosteller, and SoftMax reinforcement learning schemes. A modified version of Roth-Erev with a power exponent of 1.5, as opposed to 1 in the standard version, performs best. We also compare these reinforcement learning strategies with evolutionary schemes. This gives insight into aspects like the issue of quick adaptation as opposed to systematic exploration or the role of learning rates.

  9. Hybrid Multi-objective Forecasting of Solar Photovoltaic Output Using Kalman Filter based Interval Type-2 Fuzzy Logic System

    DEFF Research Database (Denmark)

    Hassan, Saima; Ahmadieh Khanesar, Mojtaba; Hajizadeh, Amin

    2017-01-01

    Learning of fuzzy parameters for system modeling using evolutionary algorithms is an interesting topic. In this paper, two optimal design and tuning of Interval type-2 fuzzy logic system are proposed using hybrid learning algorithms. The consequent parameters of the interval type-2 fuzzy logic...... system in both the hybrid algorithms are tuned using Kalman filter. Whereas the antecedent parameters of the system in the first hybrid algorithm is optimized using the multi-objective particle swarm optimization (MOPSO) and using the multi-objective evolutionary algorithm Based on Decomposition (MOEA...

  10. Design and implementation of fuzzy-PD controller based on relation models: A cross-entropy optimization approach

    Science.gov (United States)

    Anisimov, D. N.; Dang, Thai Son; Banerjee, Santo; Mai, The Anh

    2017-07-01

    In this paper, an intelligent system use fuzzy-PD controller based on relation models is developed for a two-wheeled self-balancing robot. Scaling factors of the fuzzy-PD controller are optimized by a Cross-Entropy optimization method. A linear Quadratic Regulator is designed to bring a comparison with the fuzzy-PD controller by control quality parameters. The controllers are ported and run on STM32F4 Discovery Kit based on the real-time operating system. The experimental results indicate that the proposed fuzzy-PD controller runs exactly on embedded system and has desired performance in term of fast response, good balance and stabilize.

  11. Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

    Science.gov (United States)

    Ferreria, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.

    2017-01-01

    Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

  12. The drift diffusion model as the choice rule in reinforcement learning.

    Science.gov (United States)

    Pedersen, Mads Lund; Frank, Michael J; Biele, Guido

    2017-08-01

    Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyperactivity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups.

  13. Multiagent Reinforcement Learning Dynamic Spectrum Access in Cognitive Radios

    Directory of Open Access Journals (Sweden)

    Wu Chun

    2014-02-01

    Full Text Available A multiuser independent Q-learning method which does not need information interaction is proposed for multiuser dynamic spectrum accessing in cognitive radios. The method adopts self-learning paradigm, in which each CR user performs reinforcement learning only through observing individual performance reward without spending communication resource on information interaction with others. The reward is defined suitably to present channel quality and channel conflict status. The learning strategy of sufficient exploration, preference for good channel, and punishment for channel conflict is designed to implement multiuser dynamic spectrum accessing. In two users two channels scenario, a fast learning algorithm is proposed and the convergence to maximal whole reward is proved. The simulation results show that, with the proposed method, the CR system can obtain convergence of Nash equilibrium with large probability and achieve great performance of whole reward.

  14. Video Demo: Deep Reinforcement Learning for Coordination in Traffic Light Control

    NARCIS (Netherlands)

    van der Pol, E.; Oliehoek, F.A.; Bosse, T.; Bredeweg, B.

    2016-01-01

    This video demonstration contrasts two approaches to coordination in traffic light control using reinforcement learning: earlier work, based on a deconstruction of the state space into a linear combination of vehicle states, and our own approach based on the Deep Q-learning algorithm.

  15. A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

    Science.gov (United States)

    Wang, Zhihao; Yi, Jing

    2016-01-01

    For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

  16. Belief reward shaping in reinforcement learning

    CSIR Research Space (South Africa)

    Marom, O

    2018-02-01

    Full Text Available A key challenge in many reinforcement learning problems is delayed rewards, which can significantly slow down learning. Although reward shaping has previously been introduced to accelerate learning by bootstrapping an agent with additional...

  17. Working Memory and Reinforcement Schedule Jointly Determine Reinforcement Learning in Children: Potential Implications for Behavioral Parent Training

    Directory of Open Access Journals (Sweden)

    Elien Segers

    2018-03-01

    Full Text Available Introduction: Behavioral Parent Training (BPT is often provided for childhood psychiatric disorders. These disorders have been shown to be associated with working memory impairments. BPT is based on operant learning principles, yet how operant principles shape behavior (through the partial reinforcement (PRF extinction effect, i.e., greater resistance to extinction that is created when behavior is reinforced partially rather than continuously and the potential role of working memory therein is scarcely studied in children. This study explored the PRF extinction effect and the role of working memory therein using experimental tasks in typically developing children.Methods: Ninety-seven children (age 6–10 completed a working memory task and an operant learning task, in which children acquired a response-sequence rule under either continuous or PRF (120 trials, followed by an extinction phase (80 trials. Data of 88 children were used for analysis.Results: The PRF extinction effect was confirmed: We observed slower acquisition and extinction in the PRF condition as compared to the continuous reinforcement (CRF condition. Working memory was negatively related to acquisition but not extinction performance.Conclusion: Both reinforcement contingencies and working memory relate to acquisition performance. Potential implications for BPT are that decreasing working memory load may enhance the chance of optimally learning through reinforcement.

  18. Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

    Science.gov (United States)

    Jiang, Yizhang; Wu, Dongrui; Deng, Zhaohong; Qian, Pengjiang; Wang, Jun; Wang, Guanjin; Chung, Fu-Lai; Choi, Kup-Sze; Wang, Shitong

    2017-12-01

    Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.

  19. Adaptive representations for reinforcement learning

    NARCIS (Netherlands)

    Whiteson, S.

    2010-01-01

    This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own

  20. Punishment Insensitivity and Impaired Reinforcement Learning in Preschoolers

    Science.gov (United States)

    Briggs-Gowan, Margaret J.; Nichols, Sara R.; Voss, Joel; Zobel, Elvira; Carter, Alice S.; McCarthy, Kimberly J.; Pine, Daniel S.; Blair, James; Wakschlag, Lauren S.

    2014-01-01

    Background: Youth and adults with psychopathic traits display disrupted reinforcement learning. Advances in measurement now enable examination of this association in preschoolers. The current study examines relations between reinforcement learning in preschoolers and parent ratings of reduced responsiveness to socialization, conceptualized as a…

  1. Reinforcement learning in continuous state and action spaces

    NARCIS (Netherlands)

    H. P. van Hasselt (Hado); M.A. Wiering; M. van Otterlo

    2012-01-01

    textabstractMany traditional reinforcement-learning algorithms have been designed for problems with small finite state and action spaces. Learning in such discrete problems can been difficult, due to noise and delayed reinforcements. However, many real-world problems have continuous state or action

  2. Reinforcement active learning in the vibrissae system: optimal object localization.

    Science.gov (United States)

    Gordon, Goren; Dorfman, Nimrod; Ahissar, Ehud

    2013-01-01

    Rats move their whiskers to acquire information about their environment. It has been observed that they palpate novel objects and objects they are required to localize in space. We analyze whisker-based object localization using two complementary paradigms, namely, active learning and intrinsic-reward reinforcement learning. Active learning algorithms select the next training samples according to the hypothesized solution in order to better discriminate between correct and incorrect labels. Intrinsic-reward reinforcement learning uses prediction errors as the reward to an actor-critic design, such that behavior converges to the one that optimizes the learning process. We show that in the context of object localization, the two paradigms result in palpation whisking as their respective optimal solution. These results suggest that rats may employ principles of active learning and/or intrinsic reward in tactile exploration and can guide future research to seek the underlying neuronal mechanisms that implement them. Furthermore, these paradigms are easily transferable to biomimetic whisker-based artificial sensors and can improve the active exploration of their environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. Neural Basis of Reinforcement Learning and Decision Making

    Science.gov (United States)

    Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan

    2012-01-01

    Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal’s knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain. PMID:22462543

  4. Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy

    Science.gov (United States)

    Shi, Yan; Lu, Zhenzhou; Zhou, Yicheng

    2018-06-01

    To analyse the component of fuzzy output entropy, a decomposition method of fuzzy output entropy is first presented. After the decomposition of fuzzy output entropy, the total fuzzy output entropy can be expressed as the sum of the component fuzzy entropy contributed by fuzzy inputs. Based on the decomposition of fuzzy output entropy, a new global sensitivity analysis model is established for measuring the effects of uncertainties of fuzzy inputs on the output. The global sensitivity analysis model can not only tell the importance of fuzzy inputs but also simultaneously reflect the structural composition of the response function to a certain degree. Several examples illustrate the validity of the proposed global sensitivity analysis, which is a significant reference in engineering design and optimization of structural systems.

  5. A modified teaching–learning based optimization for multi-objective optimal power flow problem

    International Nuclear Information System (INIS)

    Shabanpour-Haghighi, Amin; Seifi, Ali Reza; Niknam, Taher

    2014-01-01

    Highlights: • A new modified teaching–learning based algorithm is proposed. • A self-adaptive wavelet mutation strategy is used to enhance the performance. • To avoid reaching a large repository size, a fuzzy clustering technique is used. • An efficiently smart population selection is utilized. • Simulations show the superiority of this algorithm compared with other ones. - Abstract: In this paper, a modified teaching–learning based optimization algorithm is analyzed to solve the multi-objective optimal power flow problem considering the total fuel cost and total emission of the units. The modified phase of the optimization algorithm utilizes a self-adapting wavelet mutation strategy. Moreover, a fuzzy clustering technique is proposed to avoid extremely large repository size besides a smart population selection for the next iteration. These techniques make the algorithm searching a larger space to find the optimal solutions while speed of the convergence remains good. The IEEE 30-Bus and 57-Bus systems are used to illustrate performance of the proposed algorithm and results are compared with those in literatures. It is verified that the proposed approach has better performance over other techniques

  6. Multiagent-Based Simulation of Temporal-Spatial Characteristics of Activity-Travel Patterns Using Interactive Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Min Yang

    2014-01-01

    Full Text Available We propose a multiagent-based reinforcement learning algorithm, in which the interactions between travelers and the environment are considered to simulate temporal-spatial characteristics of activity-travel patterns in a city. Road congestion degree is added to the reinforcement learning algorithm as a medium that passes the influence of one traveler’s decision to others. Meanwhile, the agents used in the algorithm are initialized from typical activity patterns extracted from the travel survey diary data of Shangyu city in China. In the simulation, both macroscopic activity-travel characteristics such as traffic flow spatial-temporal distribution and microscopic characteristics such as activity-travel schedules of each agent are obtained. Comparing the simulation results with the survey data, we find that deviation of the peak-hour traffic flow is less than 5%, while the correlation of the simulated versus survey location choice distribution is over 0.9.

  7. Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach.

    Science.gov (United States)

    Lam, H K

    2012-02-01

    This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach.

  8. A neuro-fuzzy controlling algorithm for wind turbine

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Li [Tampere Univ. of Technology (Finland); Eriksson, J T [Tampere Univ. of Technology (Finland)

    1996-12-31

    The wind turbine control system is stochastic and nonlinear, offering a demanding field for different control methods. An improved and efficient controller will have great impact on the cost-effectiveness of the technology. In this article, a design method for a self-organizing fuzzy controller is discussed, which combines two popular computational intelligence techniques, neural networks and fuzzy logic. Based on acquisited dynamic parameters of the wind, it can effectively predict wind changes in speed and direction. Maximum power can always be extracted from the kinetic energy of the wind. Based on the stimulating experiments applying nonlinear dynamics to a `Variable Speed Fixed Angle` wind turbine, it is demonstrated that the proposed control model 3rd learning algorithm provide a predictable, stable and accurate performance. The robustness of the controller to system parameter variations and measurement disturbances is also discussed. (author)

  9. A neuro-fuzzy controlling algorithm for wind turbine

    Energy Technology Data Exchange (ETDEWEB)

    Li Lin [Tampere Univ. of Technology (Finland); Eriksson, J.T. [Tampere Univ. of Technology (Finland)

    1995-12-31

    The wind turbine control system is stochastic and nonlinear, offering a demanding field for different control methods. An improved and efficient controller will have great impact on the cost-effectiveness of the technology. In this article, a design method for a self-organizing fuzzy controller is discussed, which combines two popular computational intelligence techniques, neural networks and fuzzy logic. Based on acquisited dynamic parameters of the wind, it can effectively predict wind changes in speed and direction. Maximum power can always be extracted from the kinetic energy of the wind. Based on the stimulating experiments applying nonlinear dynamics to a `Variable Speed Fixed Angle` wind turbine, it is demonstrated that the proposed control model 3rd learning algorithm provide a predictable, stable and accurate performance. The robustness of the controller to system parameter variations and measurement disturbances is also discussed. (author)

  10. [Predicting Incidence of Hepatitis E in Chinausing Fuzzy Time Series Based on Fuzzy C-Means Clustering Analysis].

    Science.gov (United States)

    Luo, Yi; Zhang, Tao; Li, Xiao-song

    2016-05-01

    To explore the application of fuzzy time series model based on fuzzy c-means clustering in forecasting monthly incidence of Hepatitis E in mainland China. Apredictive model (fuzzy time series method based on fuzzy c-means clustering) was developed using Hepatitis E incidence data in mainland China between January 2004 and July 2014. The incidence datafrom August 2014 to November 2014 were used to test the fitness of the predictive model. The forecasting results were compared with those resulted from traditional fuzzy time series models. The fuzzy time series model based on fuzzy c-means clustering had 0.001 1 mean squared error (MSE) of fitting and 6.977 5 x 10⁻⁴ MSE of forecasting, compared with 0.0017 and 0.0014 from the traditional forecasting model. The results indicate that the fuzzy time series model based on fuzzy c-means clustering has a better performance in forecasting incidence of Hepatitis E.

  11. Reinforcement Learning in Repeated Portfolio Decisions

    OpenAIRE

    Diao, Linan; Rieskamp, Jörg

    2011-01-01

    How do people make investment decisions when they receive outcome feedback? We examined how well the standard mean-variance model and two reinforcement models predict people's portfolio decisions. The basic reinforcement model predicts a learning process that relies solely on the portfolio's overall return, whereas the proposed extended reinforcement model also takes the risk and covariance of the investments into account. The experimental results illustrate that people reacted sensitively to...

  12. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  13. Reinforcement learning improves behaviour from evaluative feedback

    Science.gov (United States)

    Littman, Michael L.

    2015-05-01

    Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial intelligence problem in a microcosm because learning algorithms must act autonomously to perform well and achieve their goals. Partly driven by the increasing availability of rich data, recent years have seen exciting advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology, leading to increasing applicability to real-life problems.

  14. Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

    Directory of Open Access Journals (Sweden)

    Jean Marie Vianney Kinani

    2017-01-01

    Full Text Available We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient’s response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR and fluid-attenuated inversion recovery (FLAIR images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.

  15. Solution to reinforcement learning problems with artificial potential field

    Institute of Scientific and Technical Information of China (English)

    XIE Li-juan; XIE Guang-rong; CHEN Huan-wen; LI Xiao-li

    2008-01-01

    A novel method was designed to solve reinforcement learning problems with artificial potential field. Firstly a reinforcement learning problem was transferred to a path planning problem by using artificial potential field(APF), which was a very appropriate method to model a reinforcement learning problem. Secondly, a new APF algorithm was proposed to overcome the local minimum problem in the potential field methods with a virtual water-flow concept. The performance of this new method was tested by a gridworld problem named as key and door maze. The experimental results show that within 45 trials, good and deterministic policies are found in almost all simulations. In comparison with WIERING's HQ-learning system which needs 20 000 trials for stable solution, the proposed new method can obtain optimal and stable policy far more quickly than HQ-learning. Therefore, the new method is simple and effective to give an optimal solution to the reinforcement learning problem.

  16. COMPARISON of FUZZY-BASED MODELS in LANDSLIDE HAZARD MAPPING

    Directory of Open Access Journals (Sweden)

    N. Mijani

    2017-09-01

    Full Text Available Landslide is one of the main geomorphic processes which effects on the development of prospect in mountainous areas and causes disastrous accidents. Landslide is an event which has different uncertain criteria such as altitude, slope, aspect, land use, vegetation density, precipitation, distance from the river and distance from the road network. This research aims to compare and evaluate different fuzzy-based models including Fuzzy Analytic Hierarchy Process (Fuzzy-AHP, Fuzzy Gamma and Fuzzy-OR. The main contribution of this paper reveals to the comprehensive criteria causing landslide hazard considering their uncertainties and comparison of different fuzzy-based models. The quantify of evaluation process are calculated by Density Ratio (DR and Quality Sum (QS. The proposed methodology implemented in Sari, one of the city of Iran which has faced multiple landslide accidents in recent years due to the particular environmental conditions. The achieved results of accuracy assessment based on the quantifier strated that Fuzzy-AHP model has higher accuracy compared to other two models in landslide hazard zonation. Accuracy of zoning obtained from Fuzzy-AHP model is respectively 0.92 and 0.45 based on method Precision (P and QS indicators. Based on obtained landslide hazard maps, Fuzzy-AHP, Fuzzy Gamma and Fuzzy-OR respectively cover 13, 26 and 35 percent of the study area with a very high risk level. Based on these findings, fuzzy-AHP model has been selected as the most appropriate method of zoning landslide in the city of Sari and the Fuzzy-gamma method with a minor difference is in the second order.

  17. Why fuzzy controllers should be fuzzy

    International Nuclear Information System (INIS)

    Nowe, A.

    1996-01-01

    Fuzzy controllers are usually looked at as crisp valued mappings especially when artificial intelligence learning techniques are used to build up the controller. By doing so the semantics of a fuzzy conclusion being a fuzzy restriction on the viable control actions is non-existing. In this paper the authors criticise from an approximation point of view using a fuzzy controller to express a crisp mapping does not seem the right way to go. Secondly it is illustrated that interesting information is contained in a fuzzy conclusion when indeed this conclusion is considered as a fuzzy restriction. This information turns out to be very valuable when viability problems are concerned, i.e. problems where the objective is to keep a system within predefined boundaries

  18. Learning Similar Actions by Reinforcement or Sensory-Prediction Errors Rely on Distinct Physiological Mechanisms.

    Science.gov (United States)

    Uehara, Shintaro; Mawase, Firas; Celnik, Pablo

    2017-09-14

    Humans can acquire knowledge of new motor behavior via different forms of learning. The two forms most commonly studied have been the development of internal models based on sensory-prediction errors (error-based learning) and success-based feedback (reinforcement learning). Human behavioral studies suggest these are distinct learning processes, though the neurophysiological mechanisms that are involved have not been characterized. Here, we evaluated physiological markers from the cerebellum and the primary motor cortex (M1) using noninvasive brain stimulations while healthy participants trained finger-reaching tasks. We manipulated the extent to which subjects rely on error-based or reinforcement by providing either vector or binary feedback about task performance. Our results demonstrated a double dissociation where learning the task mainly via error-based mechanisms leads to cerebellar plasticity modifications but not long-term potentiation (LTP)-like plasticity changes in M1; while learning a similar action via reinforcement mechanisms elicited M1 LTP-like plasticity but not cerebellar plasticity changes. Our findings indicate that learning complex motor behavior is mediated by the interplay of different forms of learning, weighing distinct neural mechanisms in M1 and the cerebellum. Our study provides insights for designing effective interventions to enhance human motor learning. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  19. A self-adaption compensation control for hysteresis nonlinearity in piezo-actuated stages based on Pi-sigma fuzzy neural network

    Science.gov (United States)

    Xu, Rui; Zhou, Miaolei

    2018-04-01

    Piezo-actuated stages are widely applied in the high-precision positioning field nowadays. However, the inherent hysteresis nonlinearity in piezo-actuated stages greatly deteriorates the positioning accuracy of piezo-actuated stages. This paper first utilizes a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model based on the Pi-sigma fuzzy neural network (PSFNN) to construct an online rate-dependent hysteresis model for describing the hysteresis nonlinearity in piezo-actuated stages. In order to improve the convergence rate of PSFNN and modeling precision, we adopt the gradient descent algorithm featuring three different learning factors to update the model parameters. The convergence of the NARMAX model based on the PSFNN is analyzed effectively. To ensure that the parameters can converge to the true values, the persistent excitation condition is considered. Then, a self-adaption compensation controller is designed for eliminating the hysteresis nonlinearity in piezo-actuated stages. A merit of the proposed controller is that it can directly eliminate the complex hysteresis nonlinearity in piezo-actuated stages without any inverse dynamic models. To demonstrate the effectiveness of the proposed model and control methods, a set of comparative experiments are performed on piezo-actuated stages. Experimental results show that the proposed modeling and control methods have excellent performance.

  20. A Fuzzy Logic Framework for Integrating Multiple Learned Models

    Energy Technology Data Exchange (ETDEWEB)

    Hartog, Bobi Kai Den [Univ. of Nebraska, Lincoln, NE (United States)

    1999-03-01

    The Artificial Intelligence field of Integrating Multiple Learned Models (IMLM) explores ways to combine results from sets of trained programs. Aroclor Interpretation is an ill-conditioned problem in which trained programs must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. We developed a general-purpose IMLM solution, the Combiner, and applied it to Aroclor Interpretation. The Combiner's first step, Scenario Identification (M), learns rules from very sparse, synthetic training data consisting of results from a suite of trained programs called Methods. S1 produces fuzzy belief weights for each scenario by approximately matching the rules. The Combiner's second step, Aroclor Presence Detection (AP), classifies each of three Aroclors as present or absent in a sample. The third step, Aroclor Quantification (AQ), produces quantitative values for the concentration of each Aroclor in a sample. AP and AQ use automatically learned empirical biases for each of the Methods in each scenario. Through fuzzy logic, AP and AQ combine scenario weights, automatically learned biases for each of the Methods in each scenario, and Methods' results to determine results for a sample.

  1. Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation.

    Science.gov (United States)

    Kato, Ayaka; Morita, Kenji

    2016-10-01

    It has been suggested that dopamine (DA) represents reward-prediction-error (RPE) defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of 'Go' or 'No-Go' selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1) decay-induced sustained RPE creates a gradient of 'Go' values towards a goal, and (2) value-contrasts between 'Go' and 'No-Go' are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DA's roles in motivation: (i) slowdown of behavior by post-training blockade of DA signaling, (ii) observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii) relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DA's roles in value-learning and motivation. Our results also suggest that when biological systems for value-learning

  2. Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation.

    Directory of Open Access Journals (Sweden)

    Ayaka Kato

    2016-10-01

    Full Text Available It has been suggested that dopamine (DA represents reward-prediction-error (RPE defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of 'Go' or 'No-Go' selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1 decay-induced sustained RPE creates a gradient of 'Go' values towards a goal, and (2 value-contrasts between 'Go' and 'No-Go' are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DA's roles in motivation: (i slowdown of behavior by post-training blockade of DA signaling, (ii observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DA's roles in value-learning and motivation. Our results also suggest that when biological systems

  3. FUZZY CLUSTERING BASED BAYESIAN FRAMEWORK TO PREDICT MENTAL HEALTH PROBLEMS AMONG CHILDREN

    Directory of Open Access Journals (Sweden)

    M R Sumathi

    2017-04-01

    Full Text Available According to World Health Organization, 10-20% of children and adolescents all over the world are experiencing mental disorders. Correct diagnosis of mental disorders at an early stage improves the quality of life of children and avoids complicated problems. Various expert systems using artificial intelligence techniques have been developed for diagnosing mental disorders like Schizophrenia, Depression, Dementia, etc. This study focuses on predicting basic mental health problems of children, like Attention problem, Anxiety problem, Developmental delay, Attention Deficit Hyperactivity Disorder (ADHD, Pervasive Developmental Disorder(PDD, etc. using the machine learning techniques, Bayesian Networks and Fuzzy clustering. The focus of the article is on learning the Bayesian network structure using a novel Fuzzy Clustering Based Bayesian network structure learning framework. The performance of the proposed framework was compared with the other existing algorithms and the experimental results have shown that the proposed framework performs better than the earlier algorithms.

  4. Reinforcement learning for optimal control of low exergy buildings

    International Nuclear Information System (INIS)

    Yang, Lei; Nagy, Zoltan; Goffin, Philippe; Schlueter, Arno

    2015-01-01

    Highlights: • Implementation of reinforcement learning control for LowEx Building systems. • Learning allows adaptation to local environment without prior knowledge. • Presentation of reinforcement learning control for real-life applications. • Discussion of the applicability for real-life situations. - Abstract: Over a third of the anthropogenic greenhouse gas (GHG) emissions stem from cooling and heating buildings, due to their fossil fuel based operation. Low exergy building systems are a promising approach to reduce energy consumption as well as GHG emissions. They consists of renewable energy technologies, such as PV, PV/T and heat pumps. Since careful tuning of parameters is required, a manual setup may result in sub-optimal operation. A model predictive control approach is unnecessarily complex due to the required model identification. Therefore, in this work we present a reinforcement learning control (RLC) approach. The studied building consists of a PV/T array for solar heat and electricity generation, as well as geothermal heat pumps. We present RLC for the PV/T array, and the full building model. Two methods, Tabular Q-learning and Batch Q-learning with Memory Replay, are implemented with real building settings and actual weather conditions in a Matlab/Simulink framework. The performance is evaluated against standard rule-based control (RBC). We investigated different neural network structures and find that some outperformed RBC already during the learning phase. Overall, every RLC strategy for PV/T outperformed RBC by over 10% after the third year. Likewise, for the full building, RLC outperforms RBC in terms of meeting the heating demand, maintaining the optimal operation temperature and compensating more effectively for ground heat. This allows to reduce engineering costs associated with the setup of these systems, as well as decrease the return-of-invest period, both of which are necessary to create a sustainable, zero-emission building

  5. Reinforcement learning on slow features of high-dimensional input streams.

    Directory of Open Access Journals (Sweden)

    Robert Legenstein

    Full Text Available Humans and animals are able to learn complex behaviors based on a massive stream of sensory information from different modalities. Early animal studies have identified learning mechanisms that are based on reward and punishment such that animals tend to avoid actions that lead to punishment whereas rewarded actions are reinforced. However, most algorithms for reward-based learning are only applicable if the dimensionality of the state-space is sufficiently small or its structure is sufficiently simple. Therefore, the question arises how the problem of learning on high-dimensional data is solved in the brain. In this article, we propose a biologically plausible generic two-stage learning system that can directly be applied to raw high-dimensional input streams. The system is composed of a hierarchical slow feature analysis (SFA network for preprocessing and a simple neural network on top that is trained based on rewards. We demonstrate by computer simulations that this generic architecture is able to learn quite demanding reinforcement learning tasks on high-dimensional visual input streams in a time that is comparable to the time needed when an explicit highly informative low-dimensional state-space representation is given instead of the high-dimensional visual input. The learning speed of the proposed architecture in a task similar to the Morris water maze task is comparable to that found in experimental studies with rats. This study thus supports the hypothesis that slowness learning is one important unsupervised learning principle utilized in the brain to form efficient state representations for behavioral learning.

  6. The Motion Path Study of Measuring Robot Based on Variable Universe Fuzzy Control

    Directory of Open Access Journals (Sweden)

    Ma Guoqing

    2017-01-01

    Full Text Available For the problem of measuring robot requires a higher positioning, firstly learning about the error overview of the system, analysised the influence of attitude, speed and other factors on systematic errors. Then collected and analyzed the systematic error curve in the track to complete the planning process. The last adding fuzzy control in both cases, by comparing with the original system, can found that the method based on fuzzy control system can significantly reduce the error during the motion.

  7. Self Regulated Learning for Developing Nursing Skills via Web-Based

    Science.gov (United States)

    Razak, Rafiza Abdul; Hua, Khor Bee

    2013-01-01

    The purpose of this study is to find out whether the first year student nurses able to learn and develop the psychomotor skills for basic nursing care using web-based learning environment. More importantly, the researcher investigated whether web-based learning environment using self regulated learning strategy able to help students to apply the…

  8. Fuzzy data analysis

    CERN Document Server

    Bandemer, Hans

    1992-01-01

    Fuzzy data such as marks, scores, verbal evaluations, imprecise observations, experts' opinions and grey tone pictures, are quite common. In Fuzzy Data Analysis the authors collect their recent results providing the reader with ideas, approaches and methods for processing such data when looking for sub-structures in knowledge bases for an evaluation of functional relationship, e.g. in order to specify diagnostic or control systems. The modelling presented uses ideas from fuzzy set theory and the suggested methods solve problems usually tackled by data analysis if the data are real numbers. Fuzzy Data Analysis is self-contained and is addressed to mathematicians oriented towards applications and to practitioners in any field of application who have some background in mathematics and statistics.

  9. Reinforcement Learning in Autism Spectrum Disorder

    Directory of Open Access Journals (Sweden)

    Manuela Schuetze

    2017-11-01

    Full Text Available Early behavioral interventions are recognized as integral to standard care in autism spectrum disorder (ASD, and often focus on reinforcing desired behaviors (e.g., eye contact and reducing the presence of atypical behaviors (e.g., echoing others' phrases. However, efficacy of these programs is mixed. Reinforcement learning relies on neurocircuitry that has been reported to be atypical in ASD: prefrontal-sub-cortical circuits, amygdala, brainstem, and cerebellum. Thus, early behavioral interventions rely on neurocircuitry that may function atypically in at least a subset of individuals with ASD. Recent work has investigated physiological, behavioral, and neural responses to reinforcers to uncover differences in motivation and learning in ASD. We will synthesize this work to identify promising avenues for future research that ultimately can be used to enhance the efficacy of early intervention.

  10. A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition

    OpenAIRE

    Zhang, Wei; Zhang, Xueying; Sun, Ying

    2017-01-01

    Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods do not consider the relationship between emotions. Thus, in this study, a speech emotion recognition system based on the fuzzy cognitive map (FCM) approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is proposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between e...

  11. Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning

    Directory of Open Access Journals (Sweden)

    Yuntian Feng

    2017-01-01

    Full Text Available We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.

  12. Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning.

    Science.gov (United States)

    Feng, Yuntian; Zhang, Hongjun; Hao, Wenning; Chen, Gang

    2017-01-01

    We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q -Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.

  13. The effect of web quest and team-based learning on students’ self-regulation

    Directory of Open Access Journals (Sweden)

    ZOHREH BADIYEPEYMAIE JAHROMI

    2016-04-01

    Full Text Available Introduction: In this study, the authors aimed to examine the effects of cooperative learning methods using Web Quest and team-based learning on students’ self-direction, self-regulation, and academic achievement. Methods: This is a comparative study of students taking a course in mental health and psychiatric disorders. In two consecutive years, a group of students were trained using the Web Quest approach as a teaching strategy (n=38, while the other group was taught using team-based learning (n=39. Data gathering was based on Guglielmino’s self-directed learning readiness scale (SDLRS and Buford’s self-regulation questionnaire. The data were analyzed by descriptive test using M (IQR, Wilcoxon signed-rank test, and the Mann–Whitney U-test in SPSS software, version 13. p<0.05 was considered as the significance level. Results: The results of the Mann–Whitney U test showed that the participants’ self- directed (self-management and self-regulated learning differed between the two groups (p=0.04 and p=0.01, respectively. Wilcoxon test revealed that self-directed learning indices (self-control and self-management were differed between the two strategies before and after the intervention. However, the scores related to learning (students’ final scores were higher in the WebQuest approach than in team-based learning. Conclusion: By employing modern educational approaches, students are not only more successful in their studies but also acquire the necessary professional skills for future performance. Further research to compare the effects of new methods of teaching is required.

  14. Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

    OpenAIRE

    Kidziński, Łukasz; Mohanty, Sharada Prasanna; Ong, Carmichael; Huang, Zhewei; Zhou, Shuchang; Pechenko, Anton; Stelmaszczyk, Adam; Jarosik, Piotr; Pavlov, Mikhail; Kolesnikov, Sergey; Plis, Sergey; Chen, Zhibo; Zhang, Zhizheng; Chen, Jiale; Shi, Jun

    2018-01-01

    In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar ...

  15. Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening

    OpenAIRE

    He, Frank S.; Liu, Yang; Schwing, Alexander G.; Peng, Jian

    2016-01-01

    We propose a novel training algorithm for reinforcement learning which combines the strength of deep Q-learning with a constrained optimization approach to tighten optimality and encourage faster reward propagation. Our novel technique makes deep reinforcement learning more practical by drastically reducing the training time. We evaluate the performance of our approach on the 49 games of the challenging Arcade Learning Environment, and report significant improvements in both training time and...

  16. Manifold Regularized Reinforcement Learning.

    Science.gov (United States)

    Li, Hongliang; Liu, Derong; Wang, Ding

    2018-04-01

    This paper introduces a novel manifold regularized reinforcement learning scheme for continuous Markov decision processes. Smooth feature representations for value function approximation can be automatically learned using the unsupervised manifold regularization method. The learned features are data-driven, and can be adapted to the geometry of the state space. Furthermore, the scheme provides a direct basis representation extension for novel samples during policy learning and control. The performance of the proposed scheme is evaluated on two benchmark control tasks, i.e., the inverted pendulum and the energy storage problem. Simulation results illustrate the concepts of the proposed scheme and show that it can obtain excellent performance.

  17. A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

    Science.gov (United States)

    Guan, Hongjun; Dai, Zongli; Zhao, Aiwu; He, Jie

    2018-01-01

    In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.

  18. A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

    Directory of Open Access Journals (Sweden)

    Hongjun Guan

    Full Text Available In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBPNeural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS. On this basis, the FTTS blur into fuzzy time series (FFTS based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.

  19. Outdoor altitude stabilization of QuadRotor based on type-2 fuzzy and fuzzy PID

    Science.gov (United States)

    Wicaksono, H.; Yusuf, Y. G.; Kristanto, C.; Haryanto, L.

    2017-11-01

    This paper presents a design of altitude stabilization of QuadRotor based on type-2 fuzzy and fuzzy PID. This practical design is implemented outdoor. Barometric and sonar sensor were used in this experiment as an input for the controller YoHe. The throttle signal as a control input was provided by the controller to leveling QuadRotor in particular altitude and known well as altitude stabilization. The parameter of type-2 fuzzy and fuzzy PID was tuned in several heights to get the best control parameter for any height. Type-2 fuzzy produced better result than fuzzy PID but had a slow response in the beginning.

  20. Determination of interrill soil erodibility coefficient based on Fuzzy and Fuzzy-Genetic Systems

    Directory of Open Access Journals (Sweden)

    Habib Palizvan Zand

    2017-02-01

    Full Text Available Introduction: Although the fuzzy logic science has been used successfully in various sudies of hydrology and soil erosion, but in literature review no article was found about its performance for estimating of interrill erodibility. On the other hand, studies indicate that genetic algorithm techniques can be used in fuzzy models and finding the appropriate membership functions for linguistic variables and fuzzy rules. So this study was conducted to develop the fuzzy and fuzzy–genetics models and investigation of their performance in the estimation of soil interrill erodibility factor (Ki. Materials and Methods: For this reason 36 soil samples with different physical and chemical properties were collected from west of Azerbaijan province . soilsamples were also taken from the Ap or A horizon of each soil profile. The samples were air-dried , sieved and Some soil characteristics such as soil texture, organic matter (OM, cation exchange capacity (CEC, sodium adsorption ratio (SAR, EC and pH were determined by the standard laboratory methods. Aggregates size distributions (ASD were determined by the wet-sieving method and fractal dimension of soil aggregates (Dn was also calculated. In order to determination of soil interrill erodibility, the flume experiment performed by packing soil a depth of 0.09-m in 0.5 × 1.0 m. soil was saturated from the base and adjusted to 9% slope and was subjected to at least 90 min rainfall . Rainfall intensity treatments were 20, 37 and 47 mm h-1. During each rainfall event, runoff was collected manually in different time intervals, being less than 60 s at the beginning, up to 15 min near the end of the test. At the end of the experiment, the volumes of runoff samples and the mass of sediment load at each time interval were measured. Finally interrill erodibility values were calculated using Kinnell (11 Equation. Then by statistical analyses Dn and sand percent of the soils were selected as input variables and Ki as

  1. Fuzzy Reasoning Based on First-Order Modal Logic,

    NARCIS (Netherlands)

    Zhang, Xiaoru; Zhang, Z.; Sui, Y.; Huang, Z.

    2008-01-01

    As an extension of traditional modal logics, this paper proposes a fuzzy first-order modal logic based on believable degree, and gives out a description of the fuzzy first-order modal logic based on constant domain semantics. In order to make the reasoning procedure between the fuzzy assertions

  2. Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships.

    Science.gov (United States)

    Chen, Shyi-Ming; Chen, Shen-Wen

    2015-03-01

    In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy-trend logical relationships. Firstly, the proposed method fuzzifies the historical training data of the main factor and the secondary factor into fuzzy sets, respectively, to form two-factors second-order fuzzy logical relationships. Then, it groups the obtained two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, it calculates the probability of the "down-trend," the probability of the "equal-trend" and the probability of the "up-trend" of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group, respectively. Finally, it performs the forecasting based on the probabilities of the down-trend, the equal-trend, and the up-trend of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the NTD/USD exchange rates. The experimental results show that the proposed method outperforms the existing methods.

  3. Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning.

    Science.gov (United States)

    Doll, Bradley B; Bath, Kevin G; Daw, Nathaniel D; Frank, Michael J

    2016-01-27

    Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by "model-based" learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning. Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes

  4. Reinforced Ultra-Tightly Coupled GPS/INS System for Challenging Environment

    Directory of Open Access Journals (Sweden)

    Xueyun Wang

    2014-01-01

    Full Text Available Among all integration levels currently available for Global Positioning System (GPS and Inertial Navigation System (INS Integrated System, ultra-tightly coupled (UTC GPS/INS system is the best choice for accurate and reliable navigation. Nevertheless the performance of UTC GPS/INS system degrades in challenging environments, such as jamming, changing noise of GPS signals, and high dynamic maneuvers. When low-end Inertial Measurement Units (IMUs based on MEMS sensors are employed, the performance degradation will be more severe. To solve this problem, a reinforced UTC GPS/INS system is proposed. Two techniques are adopted to deal with jamming and high dynamics. Firstly, adaptive integration Kalman filter (IKF based on fuzzy logics is developed to reinforce the antijamming ability. The parameters of membership functions (MFs are adjusted and optimized through self-developed neutral network. Secondly, a Doppler frequency error estimator based on Kalman filter is designed to improve the navigation performance under high dynamics. A complete simulation platform is established to evaluate the reinforced system. Results demonstrate that the proposed system architecture significantly improves navigation performance in challenging environments and it is a more advanced solution to accurate and reliable navigation than traditional UTC GPS/INS system.

  5. Reinforcement learning controller design for affine nonlinear discrete-time systems using online approximators.

    Science.gov (United States)

    Yang, Qinmin; Jagannathan, Sarangapani

    2012-04-01

    In this paper, reinforcement learning state- and output-feedback-based adaptive critic controller designs are proposed by using the online approximators (OLAs) for a general multi-input and multioutput affine unknown nonlinear discretetime systems in the presence of bounded disturbances. The proposed controller design has two entities, an action network that is designed to produce optimal signal and a critic network that evaluates the performance of the action network. The critic estimates the cost-to-go function which is tuned online using recursive equations derived from heuristic dynamic programming. Here, neural networks (NNs) are used both for the action and critic whereas any OLAs, such as radial basis functions, splines, fuzzy logic, etc., can be utilized. For the output-feedback counterpart, an additional NN is designated as the observer to estimate the unavailable system states, and thus, separation principle is not required. The NN weight tuning laws for the controller schemes are also derived while ensuring uniform ultimate boundedness of the closed-loop system using Lyapunov theory. Finally, the effectiveness of the two controllers is tested in simulation on a pendulum balancing system and a two-link robotic arm system.

  6. Integration of problem-based learning and innovative technology into a self-care course.

    Science.gov (United States)

    McFalls, Marsha

    2013-08-12

    To assess the integration of problem-based learning and technology into a self-care course. Problem-based learning (PBL) activities were developed and implemented in place of lectures in a self-care course. Students used technology, such as computer-generated virtual patients and iPads, during the PBL sessions. Students' scores on post-case quizzes were higher than on pre-case quizzes used to assess baseline knowledge. Student satisfaction with problem-based learning and the use of technology in the course remained consistent throughout the semester. Integrating problem-based learning and technology into a self-care course enabled students to become active learners.

  7. Fuzzy model-based control of a nuclear reactor

    International Nuclear Information System (INIS)

    Van Den Durpel, L.; Ruan, D.

    1994-01-01

    The fuzzy model-based control of a nuclear power reactor is an emerging research topic world-wide. SCK-CEN is dealing with this research in a preliminary stage, including two aspects, namely fuzzy control and fuzzy modelling. The aim is to combine both methodologies in contrast to conventional model-based PID control techniques, and to state advantages of including fuzzy parameters as safety and operator feedback. This paper summarizes the general scheme of this new research project

  8. The Role of Self-Assessment in Foundation of Mathematics Learning

    Science.gov (United States)

    Masriyah

    2018-01-01

    This research is motivated by the low performance of students who took Foundations of Mathematics course. This study was aimed to describe (1) the learning outcomes of students who learned Mathematics Foundation after learning axiomatic applying self-assessment; (2) the difficulty of students and the alternative solutions; and (3) the response of students toward Foundation of Mathematics learning taught by applying self-assessment. This research was a descriptive research. The subjects were 25 mathematics students who studied Foundation of Mathematics in odd semester of the 2015/2016 academic year. Data collection was done using questionnaires, and testing methods. Based on the results of data analysis, it can be concluded that the learning outcomes of students were categorized as “good.” Student responses were positive; the difficulties lied in the sub material: Classification of Axiom Systems and the requirements, Theorem and how the formation, and finite geometry. The alternatives deal with these difficulties are to give emphasis and explanation as needed on these materials, as well as provide some more exercises to reinforce their understanding.

  9. Decentralized Reinforcement Learning of robot behaviors

    NARCIS (Netherlands)

    Leottau, David L.; Ruiz-del-Solar, Javier; Babuska, R.

    2018-01-01

    A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individual behaviors in problems where multi-dimensional action spaces are involved. When using this methodology, sub-tasks are learned in parallel by individual agents working toward a common goal. In

  10. Fuzzy model-based observers for fault detection in CSTR.

    Science.gov (United States)

    Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

    2015-11-01

    Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  11. Self-tuning fuzzy logic control of a switched reluctance generator for wind energy applications

    DEFF Research Database (Denmark)

    Park, Kiwoo; Chen, Zhe

    2012-01-01

    determination, self-tuning FLC for speed control, and a current controller. The turn-on and turn-off angle determination, as its name implies, controls the turn-on and turn-off angles of power switches to improve the efficiency and torque regulation of the SRG. The self-tuning FLC is the speed controller which......This paper presents a new self-tuning fuzzy logic control (FLC) based speed controller of a switched reluctance generator (SRG) for wind power applications. Due to its doubly salient structure and magnetic saturation, the SRG possesses an inherent characteristic of strong nonlinearity. In addition...

  12. Correction of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEO

    Directory of Open Access Journals (Sweden)

    Juan Hernandez-Vicen

    2018-03-01

    Full Text Available New applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems related to image processing makes the application of visual information within robot control algorithms difficult. Camera-based systems have inherent errors that affect the quality and reliability of the information obtained. The need of correcting image distortion slows down image parameter computing, which decreases performance of control algorithms. In this paper, a new approach to correcting several sources of visual distortions on images in only one computing step is proposed. The goal of this system/algorithm is the computation of the tilt angle of an object transported by a robot, minimizing image inherent errors and increasing computing speed. After capturing the image, the computer system extracts the angle using a Fuzzy filter that corrects at the same time all possible distortions, obtaining the real angle in only one processing step. This filter has been developed by the means of Neuro-Fuzzy learning techniques, using datasets with information obtained from real experiments. In this way, the computing time has been decreased and the performance of the application has been improved. The resulting algorithm has been tried out experimentally in robot transportation tasks in the humanoid robot TEO (Task Environment Operator from the University Carlos III of Madrid.

  13. Neural correlates of reinforcement learning and social preferences in competitive bidding.

    Science.gov (United States)

    van den Bos, Wouter; Talwar, Arjun; McClure, Samuel M

    2013-01-30

    In competitive social environments, people often deviate from what rational choice theory prescribes, resulting in losses or suboptimal monetary gains. We investigate how competition affects learning and decision-making in a common value auction task. During the experiment, groups of five human participants were simultaneously scanned using MRI while playing the auction task. We first demonstrate that bidding is well characterized by reinforcement learning with biased reward representations dependent on social preferences. Indicative of reinforcement learning, we found that estimated trial-by-trial prediction errors correlated with activity in the striatum and ventromedial prefrontal cortex. Additionally, we found that individual differences in social preferences were related to activity in the temporal-parietal junction and anterior insula. Connectivity analyses suggest that monetary and social value signals are integrated in the ventromedial prefrontal cortex and striatum. Based on these results, we argue for a novel mechanistic account for the integration of reinforcement history and social preferences in competitive decision-making.

  14. Social Cognition as Reinforcement Learning: Feedback Modulates Emotion Inference.

    Science.gov (United States)

    Zaki, Jamil; Kallman, Seth; Wimmer, G Elliott; Ochsner, Kevin; Shohamy, Daphna

    2016-09-01

    Neuroscientific studies of social cognition typically employ paradigms in which perceivers draw single-shot inferences about the internal states of strangers. Real-world social inference features much different parameters: People often encounter and learn about particular social targets (e.g., friends) over time and receive feedback about whether their inferences are correct or incorrect. Here, we examined this process and, more broadly, the intersection between social cognition and reinforcement learning. Perceivers were scanned using fMRI while repeatedly encountering three social targets who produced conflicting visual and verbal emotional cues. Perceivers guessed how targets felt and received feedback about whether they had guessed correctly. Visual cues reliably predicted one target's emotion, verbal cues predicted a second target's emotion, and neither reliably predicted the third target's emotion. Perceivers successfully used this information to update their judgments over time. Furthermore, trial-by-trial learning signals-estimated using two reinforcement learning models-tracked activity in ventral striatum and ventromedial pFC, structures associated with reinforcement learning, and regions associated with updating social impressions, including TPJ. These data suggest that learning about others' emotions, like other forms of feedback learning, relies on domain-general reinforcement mechanisms as well as domain-specific social information processing.

  15. Human demonstrations for fast and safe exploration in reinforcement learning

    NARCIS (Netherlands)

    Schonebaum, G.K.; Junell, J.L.; van Kampen, E.

    2017-01-01

    Reinforcement learning is a promising framework for controlling complex vehicles with a high level of autonomy, since it does not need a dynamic model of the vehicle, and it is able to adapt to changing conditions. When learning from scratch, the performance of a reinforcement learning controller

  16. Adaptation in the fuzzy self-organising controller

    DEFF Research Database (Denmark)

    Jantzen, Jan; Poulsen, Niels Kjølstad

    2003-01-01

    This simulation study provides an analysis of the adaptation mechanism in the self-organising fuzzy controller, SOC. The approach is to apply a traditional adaptive control viewpoint. A simplified performance measure in the SOC controller is used in a loss function, and thus the MIT rule implies...... an update mechanism similar to the SOC update mechanism. Two simulations of proportionally controlled systems show the behaviour of the proportional gain as it adapts to a specified behaviour....

  17. Optimal Control via Reinforcement Learning with Symbolic Policy Approximation

    NARCIS (Netherlands)

    Kubalìk, Jiřì; Alibekov, Eduard; Babuska, R.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

    2017-01-01

    Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value function and policy mappings. This paper

  18. Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity model

    Directory of Open Access Journals (Sweden)

    Salha M. Alzahrani

    2015-07-01

    Full Text Available Highly obfuscated plagiarism cases contain unseen and obfuscated texts, which pose difficulties when using existing plagiarism detection methods. A fuzzy semantic-based similarity model for uncovering obfuscated plagiarism is presented and compared with five state-of-the-art baselines. Semantic relatedness between words is studied based on the part-of-speech (POS tags and WordNet-based similarity measures. Fuzzy-based rules are introduced to assess the semantic distance between source and suspicious texts of short lengths, which implement the semantic relatedness between words as a membership function to a fuzzy set. In order to minimize the number of false positives and false negatives, a learning method that combines a permission threshold and a variation threshold is used to decide true plagiarism cases. The proposed model and the baselines are evaluated on 99,033 ground-truth annotated cases extracted from different datasets, including 11,621 (11.7% handmade paraphrases, 54,815 (55.4% artificial plagiarism cases, and 32,578 (32.9% plagiarism-free cases. We conduct extensive experimental verifications, including the study of the effects of different segmentations schemes and parameter settings. Results are assessed using precision, recall, F-measure and granularity on stratified 10-fold cross-validation data. The statistical analysis using paired t-tests shows that the proposed approach is statistically significant in comparison with the baselines, which demonstrates the competence of fuzzy semantic-based model to detect plagiarism cases beyond the literal plagiarism. Additionally, the analysis of variance (ANOVA statistical test shows the effectiveness of different segmentation schemes used with the proposed approach.

  19. Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning

    Science.gov (United States)

    Kastner, Lucas; Kube, Jana; Villringer, Arno; Neumann, Jane

    2017-01-01

    Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1) Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2) Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3) Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning. PMID:29163004

  20. Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Lucas Kastner

    2017-10-01

    Full Text Available Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1 Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2 Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3 Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning.

  1. Teaching Self-Control with Qualitatively Different Reinforcers

    Science.gov (United States)

    Passage, Michael; Tincani, Matt; Hantula, Donald A.

    2012-01-01

    This study examined the effectiveness of using qualitatively different reinforcers to teach self-control to an adolescent boy who had been diagnosed with an intellectual disability. First, he was instructed to engage in an activity without programmed reinforcement. Next, he was instructed to engage in the activity under a two-choice fixed-duration…

  2. Reinforcement Learning in Continuous Action Spaces

    NARCIS (Netherlands)

    Hasselt, H. van; Wiering, M.A.

    2007-01-01

    Quite some research has been done on Reinforcement Learning in continuous environments, but the research on problems where the actions can also be chosen from a continuous space is much more limited. We present a new class of algorithms named Continuous Actor Critic Learning Automaton (CACLA)

  3. Fuzzy Sets-based Control Rules for Terminating Algorithms

    Directory of Open Access Journals (Sweden)

    Jose L. VERDEGAY

    2002-01-01

    Full Text Available In this paper some problems arising in the interface between two different areas, Decision Support Systems and Fuzzy Sets and Systems, are considered. The Model-Base Management System of a Decision Support System which involves some fuzziness is considered, and in that context the questions on the management of the fuzziness in some optimisation models, and then of using fuzzy rules for terminating conventional algorithms are presented, discussed and analyzed. Finally, for the concrete case of the Travelling Salesman Problem, and as an illustration of determination, management and using the fuzzy rules, a new algorithm easy to implement in the Model-Base Management System of any oriented Decision Support System is shown.

  4. Reinforcement learning for a biped robot based on a CPG-actor-critic method.

    Science.gov (United States)

    Nakamura, Yutaka; Mori, Takeshi; Sato, Masa-aki; Ishii, Shin

    2007-08-01

    Animals' rhythmic movements, such as locomotion, are considered to be controlled by neural circuits called central pattern generators (CPGs), which generate oscillatory signals. Motivated by this biological mechanism, studies have been conducted on the rhythmic movements controlled by CPG. As an autonomous learning framework for a CPG controller, we propose in this article a reinforcement learning method we call the "CPG-actor-critic" method. This method introduces a new architecture to the actor, and its training is roughly based on a stochastic policy gradient algorithm presented recently. We apply this method to an automatic acquisition problem of control for a biped robot. Computer simulations show that training of the CPG can be successfully performed by our method, thus allowing the biped robot to not only walk stably but also adapt to environmental changes.

  5. Fast Conflict Resolution Based on Reinforcement Learning in Multi-agent System

    Institute of Scientific and Technical Information of China (English)

    PIAOSonghao; HONGBingrong; CHUHaitao

    2004-01-01

    In multi-agent system where each agen thas a different goal (even the team of agents has the same goal), agents must be able to resolve conflicts arising in the process of achieving their goal. Many researchers presented methods for conflict resolution, e.g., Reinforcement learning (RL), but the conventional RL requires a large computation cost because every agent must learn, at the same time the overlap of actions selected by each agent results in local conflict. Therefore in this paper, we propose a novel method to solve these problems. In order to deal with the conflict within the multi-agent system, the concept of potential field function based Action selection priority level (ASPL) is brought forward. In this method, all kinds of environment factor that may have influence on the priority are effectively computed with the potential field function. So the priority to access the local resource can be decided rapidly. By avoiding the complex coordination mechanism used in general multi-agent system, the conflict in multi-agent system is settled more efficiently. Our system consists of RL with ASPL module and generalized rules module. Using ASPL, RL module chooses a proper cooperative behavior, and generalized rule module can accelerate the learning process. By applying the proposed method to Robot Soccer, the learning process can be accelerated. The results of simulation and real experiments indicate the effectiveness of the method.

  6. Fuzzy Mutual Information Based min-Redundancy and Max-Relevance Heterogeneous Feature Selection

    Directory of Open Access Journals (Sweden)

    Daren Yu

    2011-08-01

    Full Text Available Feature selection is an important preprocessing step in pattern classification and machine learning, and mutual information is widely used to measure relevance between features and decision. However, it is difficult to directly calculate relevance between continuous or fuzzy features using mutual information. In this paper we introduce the fuzzy information entropy and fuzzy mutual information for computing relevance between numerical or fuzzy features and decision. The relationship between fuzzy information entropy and differential entropy is also discussed. Moreover, we combine fuzzy mutual information with qmin-Redundancy-Max-Relevanceq, qMax-Dependencyq and min-Redundancy-Max-Dependencyq algorithms. The performance and stability of the proposed algorithms are tested on benchmark data sets. Experimental results show the proposed algorithms are effective and stable.

  7. Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

    Science.gov (United States)

    Kong, Zehui; Zou, Yuan; Liu, Teng

    2017-01-01

    To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

  8. Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

    Directory of Open Access Journals (Sweden)

    Zehui Kong

    Full Text Available To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM of power-request is derived. The reinforcement learning (RL is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

  9. Human reinforcement learning subdivides structured action spaces by learning effector-specific values.

    Science.gov (United States)

    Gershman, Samuel J; Pesaran, Bijan; Daw, Nathaniel D

    2009-10-28

    Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable because of the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning-such as prediction error signals for action valuation associated with dopamine and the striatum-can cope with this "curse of dimensionality." We propose a reinforcement learning framework that allows for learned action valuations to be decomposed into effector-specific components when appropriate to a task, and test it by studying to what extent human behavior and blood oxygen level-dependent (BOLD) activity can exploit such a decomposition in a multieffector choice task. Subjects made simultaneous decisions with their left and right hands and received separate reward feedback for each hand movement. We found that choice behavior was better described by a learning model that decomposed the values of bimanual movements into separate values for each effector, rather than a traditional model that treated the bimanual actions as unitary with a single value. A decomposition of value into effector-specific components was also observed in value-related BOLD signaling, in the form of lateralized biases in striatal correlates of prediction error and anticipatory value correlates in the intraparietal sulcus. These results suggest that the human brain can use decomposed value representations to "divide and conquer" reinforcement learning over high-dimensional action spaces.

  10. Rethinking the globalisation of problem-based learning: how culture challenges self-directed learning.

    NARCIS (Netherlands)

    Frambach, J.M.; Driessen, E.W.; Chan, L.C.; Vleuten, C.P.M. van der

    2012-01-01

    Medical Education 2012: 46: 738-747 Context Medical schools worldwide are increasingly switching to student-centred methods such as problem-based learning (PBL) to foster lifelong self-directed learning (SDL). The cross-cultural applicability of these methods has been questioned because of their

  11. The effect of web quest and team-based learning on students' self-regulation.

    Science.gov (United States)

    Badiyepeymaie Jahromi, Zohreh; Mosalanejad, Leili; Rezaee, Rita

    2016-04-01

    In this study, the authors aimed to examine the effects of cooperative learning methods using Web Quest and team-based learning on students' self-direction, self-regulation, and academic achievement. This is a comparative study of students taking a course in mental health and psychiatric disorders. In two consecutive years, a group of students were trained using the WebQuest approach as a teaching strategy (n = 38), while the other group was taught using team-based learning (n=39). Data gathering was based on Guglielmino's self-directed learning readiness scale (SDLRS) and Buford's self-regulation questionnaire. The data were analyzed by descriptive test using M (IQR), Wilcoxon signed-rank test, and the Mann-Whitney U-test in SPSS software, version 13. pWebQuest approach than in team-based learning. By employing modern educational approaches, students are not only more successful in their studies but also acquire the necessary professional skills for future performance. Further research to compare the effects of new methods of teaching is required.

  12. Evolutionary computation for reinforcement learning

    NARCIS (Netherlands)

    Whiteson, S.; Wiering, M.; van Otterlo, M.

    2012-01-01

    Algorithms for evolutionary computation, which simulate the process of natural selection to solve optimization problems, are an effective tool for discovering high-performing reinforcement-learning policies. Because they can automatically find good representations, handle continuous action spaces,

  13. Prediction of drug synergy in cancer using ensemble-based machine learning techniques

    Science.gov (United States)

    Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

    2018-04-01

    Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

  14. What Can Reinforcement Learning Teach Us About Non-Equilibrium Quantum Dynamics

    Science.gov (United States)

    Bukov, Marin; Day, Alexandre; Sels, Dries; Weinberg, Phillip; Polkovnikov, Anatoli; Mehta, Pankaj

    Equilibrium thermodynamics and statistical physics are the building blocks of modern science and technology. Yet, our understanding of thermodynamic processes away from equilibrium is largely missing. In this talk, I will reveal the potential of what artificial intelligence can teach us about the complex behaviour of non-equilibrium systems. Specifically, I will discuss the problem of finding optimal drive protocols to prepare a desired target state in quantum mechanical systems by applying ideas from Reinforcement Learning [one can think of Reinforcement Learning as the study of how an agent (e.g. a robot) can learn and perfect a given policy through interactions with an environment.]. The driving protocols learnt by our agent suggest that the non-equilibrium world features possibilities easily defying intuition based on equilibrium physics.

  15. Design of fuzzy systems using neurofuzzy networks.

    Science.gov (United States)

    Figueiredo, M; Gomide, F

    1999-01-01

    This paper introduces a systematic approach for fuzzy system design based on a class of neural fuzzy networks built upon a general neuron model. The network structure is such that it encodes the knowledge learned in the form of if-then fuzzy rules and processes data following fuzzy reasoning principles. The technique provides a mechanism to obtain rules covering the whole input/output space as well as the membership functions (including their shapes) for each input variable. Such characteristics are of utmost importance in fuzzy systems design and application. In addition, after learning, it is very simple to extract fuzzy rules in the linguistic form. The network has universal approximation capability, a property very useful in, e.g., modeling and control applications. Here we focus on function approximation problems as a vehicle to illustrate its usefulness and to evaluate its performance. Comparisons with alternative approaches are also included. Both, nonnoisy and noisy data have been studied and considered in the computational experiments. The neural fuzzy network developed here and, consequently, the underlying approach, has shown to provide good results from the accuracy, complexity, and system design points of view.

  16. Rough Set Theory Based Fuzzy TOPSIS on Serious Game Design Evaluation Framework

    Directory of Open Access Journals (Sweden)

    Chung-Ho Su

    2013-01-01

    Full Text Available This study presents a hybrid methodology for solving the serious game design evaluation in which evaluation criteria are based on meaningful learning, ARCS motivation, cognitive load, and flow theory (MACF by rough set theory (RST and experts’ selection. The purpose of this study tends to develop an evaluation model with RST based fuzzy Delphi-AHP-TOPSIS for MACF characteristics. Fuzzy Delphi method is utilized for selecting the evaluation criteria, Fuzzy AHP is used for analyzing the criteria structure and determining the evaluation weight of criteria, and Fuzzy TOPSIS is applied to determine the sequence of the evaluations. A real case is also used for evaluating the selection of MACF criteria design for four serious games, and both the practice and evaluation of the case could be explained. The results show that the playfulness (C24, skills (C22, attention (C11, and personalized (C35 are determined as the four most important criteria in the MACF selection process. And evaluation results of case study point out that Game 1 has the best score overall (Game 1 > Game 3 > Game 2 > Game 4. Finally, proposed evaluation framework tends to evaluate the effectiveness and the feasibility of the evaluation model and provide design criteria for relevant multimedia game design educators.

  17. Self regulated learning trough project base learning on the prospective math teacher

    Science.gov (United States)

    Laelasari

    2018-03-01

    Development of planning, strategy, and learning activities is strongly influenced by metacognition ability, knowledge of learning strategy, and understanding of context is the most important thing to be mastered by a prospective teacher. Self-regulation owned by the individual can control behavior, and manipulate a behavior by using the ability of his mind so that individuals can react to their environment. Self-regulation is the basis of the socialization process as it relates to the entire domain of physical, cognitive, social, and emotional development. This research is a qualitative research with research subject of the fourth-semester student of class A, at one of a university in Cirebon City, West Java. In this research, the lecture material discussed is The Development of Teaching Materials, which is the subject matter that must be mastered by prospective teachers, especially teachers of mathematics education. The instrument used is the questionnaire. The results showed that through project based learning, can grow student’s self-regulated learning especially the prospective math teacher, and can be used as an alternative to the delivery of lecture materials.

  18. Self-compacting fibre-reinforced concrete

    NARCIS (Netherlands)

    Grunewald, S.; Walraven, J.C.

    2001-01-01

    The project 'self-compacting fibre-reinforced concrete (SCFRC)' is part of the Dutch STW/PPM program - 'cement-bonded materials' - DCT.4010. Subproject III to which the project ,SCFRC' belongs deals with the development of new high performance concretes. The project 'SCFRC' aims at investigating the

  19. A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Qing Ye

    2015-01-01

    Full Text Available This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach.

  20. Combined heuristic with fuzzy system to transmission system expansion planning

    Energy Technology Data Exchange (ETDEWEB)

    Silva Sousa, Aldir; Asada, Eduardo N. [University of Sao Paulo, Sao Carlos School of Engineering, Department of Electrical Engineering Av. Trabalhador Sao-carlense, 400, 13566-590 Sao Carlos, SP (Brazil)

    2011-01-15

    A heuristic algorithm that employs fuzzy logic is proposed to the power system transmission expansion planning problem. The algorithm is based on the divide to conquer strategy, which is controlled by the fuzzy system. The algorithm provides high quality solutions with the use of fuzzy decision making, which is based on nondeterministic criteria to guide the search. The fuzzy system provides a self-adjusting mechanism that eliminates the manual adjustment of parameters to each system being solved. (author)

  1. Vicarious Reinforcement In Rhesus Macaques (Macaca mulatta

    Directory of Open Access Journals (Sweden)

    Steve W. C. Chang

    2011-03-01

    Full Text Available What happens to others profoundly influences our own behavior. Such other-regarding outcomes can drive observational learning, as well as motivate cooperation, charity, empathy, and even spite. Vicarious reinforcement may serve as one of the critical mechanisms mediating the influence of other-regarding outcomes on behavior and decision-making in groups. Here we show that rhesus macaques spontaneously derive vicarious reinforcement from observing rewards given to another monkey, and that this reinforcement can motivate them to subsequently deliver or withhold rewards from the other animal. We exploited Pavlovian and instrumental conditioning to associate rewards to self (M1 and/or rewards to another monkey (M2 with visual cues. M1s made more errors in the instrumental trials when cues predicted reward to M2 compared to when cues predicted reward to M1, but made even more errors when cues predicted reward to no one. In subsequent preference tests between pairs of conditioned cues, M1s preferred cues paired with reward to M2 over cues paired with reward to no one. By contrast, M1s preferred cues paired with reward to self over cues paired with reward to both monkeys simultaneously. Rates of attention to M2 strongly predicted the strength and valence of vicarious reinforcement. These patterns of behavior, which were absent in nonsocial control trials, are consistent with vicarious reinforcement based upon sensitivity to observed, or counterfactual, outcomes with respect to another individual. Vicarious reward may play a critical role in shaping cooperation and competition, as well as motivating observational learning and group coordination in rhesus macaques, much as it does in humans. We propose that vicarious reinforcement signals mediate these behaviors via homologous neural circuits involved in reinforcement learning and decision-making.

  2. Vicarious reinforcement in rhesus macaques (macaca mulatta).

    Science.gov (United States)

    Chang, Steve W C; Winecoff, Amy A; Platt, Michael L

    2011-01-01

    What happens to others profoundly influences our own behavior. Such other-regarding outcomes can drive observational learning, as well as motivate cooperation, charity, empathy, and even spite. Vicarious reinforcement may serve as one of the critical mechanisms mediating the influence of other-regarding outcomes on behavior and decision-making in groups. Here we show that rhesus macaques spontaneously derive vicarious reinforcement from observing rewards given to another monkey, and that this reinforcement can motivate them to subsequently deliver or withhold rewards from the other animal. We exploited Pavlovian and instrumental conditioning to associate rewards to self (M1) and/or rewards to another monkey (M2) with visual cues. M1s made more errors in the instrumental trials when cues predicted reward to M2 compared to when cues predicted reward to M1, but made even more errors when cues predicted reward to no one. In subsequent preference tests between pairs of conditioned cues, M1s preferred cues paired with reward to M2 over cues paired with reward to no one. By contrast, M1s preferred cues paired with reward to self over cues paired with reward to both monkeys simultaneously. Rates of attention to M2 strongly predicted the strength and valence of vicarious reinforcement. These patterns of behavior, which were absent in non-social control trials, are consistent with vicarious reinforcement based upon sensitivity to observed, or counterfactual, outcomes with respect to another individual. Vicarious reward may play a critical role in shaping cooperation and competition, as well as motivating observational learning and group coordination in rhesus macaques, much as it does in humans. We propose that vicarious reinforcement signals mediate these behaviors via homologous neural circuits involved in reinforcement learning and decision-making.

  3. Fuzzy control in robot-soccer, evolutionary learning in the first layer of control

    Directory of Open Access Journals (Sweden)

    Peter J Thomas

    2003-02-01

    Full Text Available In this paper an evolutionary algorithm is developed to learn a fuzzy knowledge base for the control of a soccer playing micro-robot from any configuration belonging to a grid of initial configurations to hit the ball along the ball to goal line of sight. The knowledge base uses relative co-ordinate system including left and right wheel velocities of the robot. Final path positions allow forward and reverse facing robot to ball and include its physical dimensions.

  4. Introduction to Fuzzy Set Theory

    Science.gov (United States)

    Kosko, Bart

    1990-01-01

    An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

  5. CAPES: Unsupervised Storage Performance Tuning Using Neural Network-Based Deep Reinforcement Learning

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Parameter tuning is an important task of storage performance optimization. Current practice usually involves numerous tweak-benchmark cycles that are slow and costly. To address this issue, we developed CAPES, a model-less deep reinforcement learning-based unsupervised parameter tuning system driven by a deep neural network (DNN). It is designed to nd the optimal values of tunable parameters in computer systems, from a simple client-server system to a large data center, where human tuning can be costly and often cannot achieve optimal performance. CAPES takes periodic measurements of a target computer system’s state, and trains a DNN which uses Q-learning to suggest changes to the system’s current parameter values. CAPES is minimally intrusive, and can be deployed into a production system to collect training data and suggest tuning actions during the system’s daily operation. Evaluation of a prototype on a Lustre system demonstrates an increase in I/O throughput up to 45% at saturation point. About the...

  6. Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective

    Directory of Open Access Journals (Sweden)

    Zhicong Zhang

    2018-01-01

    Full Text Available We study an online multisource multisink queueing network control problem characterized with self-organizing network structure and self-organizing job routing. We decompose the self-organizing queueing network control problem into a series of interrelated Markov Decision Processes and construct a control decision model for them based on the coupled reinforcement learning (RL architecture. To maximize the mean time averaged weighted throughput of the jobs through the network, we propose a reinforcement learning algorithm with time averaged reward to deal with the control decision model and obtain a control policy integrating the jobs routing selection strategy and the jobs sequencing strategy. Computational experiments verify the learning ability and the effectiveness of the proposed reinforcement learning algorithm applied in the investigated self-organizing network control problem.

  7. Fuzzy Expert System to Characterize Students

    Science.gov (United States)

    Van Hecke, T.

    2011-01-01

    Students wanting to succeed in higher education are required to adopt an adequate learning approach. By analyzing individual learning characteristics, teachers can give personal advice to help students identify their learning success factors. An expert system based on fuzzy logic can provide economically viable solutions to help students identify…

  8. Application of Bipolar Fuzzy Sets in Graph Structures

    Directory of Open Access Journals (Sweden)

    Muhammad Akram

    2016-01-01

    Full Text Available A graph structure is a useful tool in solving the combinatorial problems in different areas of computer science and computational intelligence systems. In this paper, we apply the concept of bipolar fuzzy sets to graph structures. We introduce certain notions, including bipolar fuzzy graph structure (BFGS, strong bipolar fuzzy graph structure, bipolar fuzzy Ni-cycle, bipolar fuzzy Ni-tree, bipolar fuzzy Ni-cut vertex, and bipolar fuzzy Ni-bridge, and illustrate these notions by several examples. We study ϕ-complement, self-complement, strong self-complement, and totally strong self-complement in bipolar fuzzy graph structures, and we investigate some of their interesting properties.

  9. Human reinforcement learning subdivides structured action spaces by learning effector-specific values

    OpenAIRE

    Gershman, Samuel J.; Pesaran, Bijan; Daw, Nathaniel D.

    2009-01-01

    Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable, due to the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning – such as prediction error signals for action valuation associated with dopamine and the striatum – can cope with this “curse of dimensionality...

  10. A fuzzy ontology modeling for case base knowledge in diabetes mellitus domain

    Directory of Open Access Journals (Sweden)

    Shaker El-Sappagh

    2017-06-01

    Full Text Available Knowledge-Intensive Case-Based Reasoning Systems (KI-CBR mainly depend on ontologies. Ontology can play the role of case-base knowledge. The combination of ontology and fuzzy logic reasoning is critical in the medical domain. Case-base representation based on fuzzy ontology is expected to enhance the semantic and storage of CBR knowledge-base. This paper provides an advancement to the research of diabetes diagnosis CBR by proposing a novel case-base fuzzy OWL2 ontology (CBRDiabOnto. This ontology can be considered as the first fuzzy case-base ontology in the medical domain. It is based on a case-base fuzzy Extended Entity Relation (EER data model. It contains 63 (fuzzy classes, 54 (fuzzy object properties, 138 (fuzzy datatype properties, and 105 fuzzy datatypes. We populated the ontology with 60 cases and used SPARQL-DL for its query. The evaluation of CBRDiabOnto shows that it is accurate, consistent, and cover terminologies and logic of diabetes mellitus diagnosis.

  11. Compositions of fuzzy relations applied to veryfication learning outcomes on the example of the major “Geodesy and Cartography”

    Directory of Open Access Journals (Sweden)

    A. Mreła

    2015-05-01

        Abstract The paper presents discussion about using mathematical functions in order to help academic teachers to verify acquirement of learning outcomes by students on the example of the major “geodesy and cartography”. It is relatively easy to build fuzzy relation describing levels of realization and validation learning outcomes during subject examinations and the fuzzy relation with students’ grades is already built by teachers, the problem is to combine these two relations to get one which describes the level of acquiring learning outcomes by students. There are two main requirements facing this combinations and the paper shows that the best combination according to these requirements is algebraic composition.   Keywords: learning outcome, fuzzy relation, algebraic composition.

  12. New backpropagation algorithm with type-2 fuzzy weights for neural networks

    CERN Document Server

    Gaxiola, Fernando; Valdez, Fevrier

    2016-01-01

    In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights. The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for ô=17) and Dow-Jones time series, and recognition of person with iris bi...

  13. Fuzzy knowledge bases integration based on ontology

    OpenAIRE

    Ternovoy, Maksym; Shtogrina, Olena

    2012-01-01

    the paper describes the approach for fuzzy knowledge bases integration with the usage of ontology. This approach is based on metadata-base usage for integration of different knowledge bases with common ontology. The design process of metadata-base is described.

  14. A Fuzzy Knowledge Representation Model for Student Performance Assessment

    DEFF Research Database (Denmark)

    Badie, Farshad

    Knowledge representation models based on Fuzzy Description Logics (DLs) can provide a foundation for reasoning in intelligent learning environments. While basic DLs are suitable for expressing crisp concepts and binary relationships, Fuzzy DLs are capable of processing degrees of truth/completene...

  15. The effect of web quest and team-based learning on students’ self-regulation

    Science.gov (United States)

    BADIYEPEYMAIE JAHROMI, ZOHREH; MOSALANEJAD, LEILI; REZAEE, RITA

    2016-01-01

    Introduction In this study, the authors aimed to examine the effects of cooperative learning methods using Web Quest and team-based learning on students’ self-direction, self-regulation, and academic achievement. Method This is a comparative study of students taking a course in mental health and psychiatric disorders. In two consecutive years, a group of students were trained using the WebQuest approach as a teaching strategy (n = 38), while the other group was taught using team-based learning (n=39). Data gathering was based on Guglielmino’s self-directed learning readiness scale (SDLRS) and Buford’s self-regulation questionnaire. The data were analyzed by descriptive test using M (IQR), Wilcoxon signed-rank test, and the Mann–Whitney U-test in SPSS software, version 13. pWebQuest approach than in team-based learning. Conclusion By employing modern educational approaches, students are not only more successful in their studies but also acquire the necessary professional skills for future performance. Further research to compare the effects of new methods of teaching is required. PMID:27104202

  16. Fuzzy control. Fundamentals, stability and design of fuzzy controllers

    Energy Technology Data Exchange (ETDEWEB)

    Michels, K. [Fichtner GmbH und Co. KG, Stuttgart (Germany); Klawonn, F. [Fachhochschule Braunschweig/Wolfenbuettel (Germany). Fachbereich Informatik; Kruse, R. [Magdeburg Univ. (Germany). Fakultaet Informatik, Abt. Wiss.- und Sprachverarbeitung; Nuernberger, A. (eds.) [California Univ., Berkeley, CA (United States). Computer Science Division

    2006-07-01

    The book provides a critical discussion of fuzzy controllers from the perspective of classical control theory. Special emphases are placed on topics that are of importance for industrial applications, like (self-) tuning of fuzzy controllers, optimisation and stability analysis. The book is written as a textbook for graduate students as well as a comprehensive reference book about fuzzy control for researchers and application engineers. Starting with a detailed introduction to fuzzy systems and control theory the reader is guided to up-to-date research results. (orig.)

  17. Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

    Science.gov (United States)

    Wang, Huiya; Feng, Jun; Wang, Hongyu

    2017-07-20

    Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

  18. Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

    Science.gov (United States)

    Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

    2014-03-01

    This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case-based

  19. Learning User Preferences in Ubiquitous Systems: A User Study and a Reinforcement Learning Approach

    OpenAIRE

    Zaidenberg , Sofia; Reignier , Patrick; Mandran , Nadine

    2010-01-01

    International audience; Our study concerns a virtual assistant, proposing services to the user based on its current perceived activity and situation (ambient intelligence). Instead of asking the user to define his preferences, we acquire them automatically using a reinforcement learning approach. Experiments showed that our system succeeded the learning of user preferences. In order to validate the relevance and usability of such a system, we have first conducted a user study. 26 non-expert s...

  20. Fuzzeval: A Fuzzy Controller-Based Approach in Adaptive Learning for Backgammon Game

    DEFF Research Database (Denmark)

    Heinze, Mikael; Ortiz-Arroyo, Daniel; Larsen, Henrik Legind

    2005-01-01

    In this paper we investigate the effectiveness of applying fuzzy controllers to create strong computer player programs in the domain of backgammon. Fuzzeval, our proposed mechanism, consists of a fuzzy controller that dynamically evaluates the perceived strength of the board configurations it re-...

  1. The Effect Of Problem Based Learning And Self-Assessment On Students’ Writing Competency And Self-Regulated Learningm

    Directory of Open Access Journals (Sweden)

    Suyoga Dharma I Putu

    2018-01-01

    Full Text Available This experimental study aimed at investigating the effect of Problem Based Learning (PBL and self-assessment (SA on students’ writing competency and self-regulated learning in Tabanan Regency. This research applied 2x2 factorial design. 96 students were selected as sample through random sampling. Data were collected by test (writing competency and questionnaire (self-regulation. Students’ writings were scored by analytical scoring rubric. The obtained data were analyzed statistically by MANOVA at 5% significance level. This research discovers: 1 there is a significant effect of PBL which occurs simultaneously and separately on students’ writing competency and self-regulated learning, 2 there is a significant effect of SA which ocurs simultaneously and separately on students’ writing competency and self-regulated learning, 3 there is a significant interaction between teaching model and assessment type on students’ writing competency and self-regulated learning which occurs simultaneously, 4 there is no significant interaction between teaching model and assessment type on students’ writing competency, and 5 there is a significant interaction between teaching model and assessment type on students’ self-regulated learning. This research results implies that PBL and SA should be applied in instruction process as a way to improve the quality of students’ writing competency and self-regulated learning.

  2. The Effectiveness of Collaborative Academic Online Based Learning through Students’ Self-Regulated Learning

    Directory of Open Access Journals (Sweden)

    Erfan Priyambodo

    2016-11-01

    Full Text Available Nowdays, learning through e-learning is going rapidly, including the application BeSmart UNY. This application is providing collaborative method in teaching and learning. The aim of this study was to determine the effectiveness of the Collaborative Academic Online Based Learning method in teaching and learning toward students’ Self-Regulated Learning (SRL on Vocational School Chemistry courses. This study was quasi-experimental research method with one group pretest posttest design. Instruments used in this study were lesson plan and questionnaire of students’ SRL. This questionnaire is filled by students through BeSmart UNY.  In determining the differences SRL before and after teaching and learning processes, the data was analized by stastitical method.  The results showed that the implementation of the Collaborative Academic Online Based Learning method in teaching and learning was effective for improving students’ SRL.

  3. Fuzzy Adaptation Algorithms’ Control for Robot Manipulators with Uncertainty Modelling Errors

    Directory of Open Access Journals (Sweden)

    Yongqing Fan

    2018-01-01

    Full Text Available A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators’ system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed-loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example.

  4. Flexural strength of self compacting fiber reinforced concrete beams using polypropylene fiber: An experimental study

    Science.gov (United States)

    Lisantono, Ade; Praja, Baskoro Abdi; Hermawan, Billy Nouwen

    2017-11-01

    One of the methods to increase the tensile strength of concrete is adding a fiber material into the concrete. While to reduce a noise in a construction project, a self compacting concrete was a good choices in the project. This paper presents an experimental study of flexural behavior and strength of self compacting fiber reinforced concrete (RC) beams using polypropylene fiber. The micro monofilament polypropylene fibers with the proportion 0.9 kg/m3 of concrete weight were used in this study. Four beam specimens were cast and tested in this study. Two beams were cast of self compacting reinforced concrete without fiber, and two beams were cast of self compacting fiber reinforced concrete using polypropylene. The beams specimen had the section of (180×260) mm and the length was 2000 mm. The beams had simple supported with the span of 1800 mm. The longitudinal reinforcements were using diameter of 10 mm. Two reinforcements of Ø10 mm were put for compressive reinforcement and three reinforcements of Ø10 mm were put for tensile reinforcement. The shear reinforcement was using diameter of 8 mm. The shear reinforcements with spacing of 100 mm were put in the one fourth near to the support and the spacing of 150 mm were put in the middle span. Two points loading were used in the testing. The result shows that the load-carrying capacity of the self compacting reinforced concrete beam using polypropylene was a little bit higher than the self compacting reinforced concrete beam without polypropylene. The increment of load-carrying capacity of self compacting polypropylene fiber reinforced concrete was not so significant because the increment was only 2.80 % compare to self compacting non fiber reinforced concrete. And from the load-carrying capacity-deflection relationship curves show that both the self compacting polypropylene fiber reinforced concrete beam and the self compacting non fiber reinforced concrete beam were ductile beams.

  5. Fuzzy rule-based model for hydropower reservoirs operation

    Energy Technology Data Exchange (ETDEWEB)

    Moeini, R.; Afshar, A.; Afshar, M.H. [School of Civil Engineering, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of)

    2011-02-15

    Real-time hydropower reservoir operation is a continuous decision-making process of determining the water level of a reservoir or the volume of water released from it. The hydropower operation is usually based on operating policies and rules defined and decided upon in strategic planning. This paper presents a fuzzy rule-based model for the operation of hydropower reservoirs. The proposed fuzzy rule-based model presents a set of suitable operating rules for release from the reservoir based on ideal or target storage levels. The model operates on an 'if-then' principle, in which the 'if' is a vector of fuzzy premises and the 'then' is a vector of fuzzy consequences. In this paper, reservoir storage, inflow, and period are used as premises and the release as the consequence. The steps involved in the development of the model include, construction of membership functions for the inflow, storage and the release, formulation of fuzzy rules, implication, aggregation and defuzzification. The required knowledge bases for the formulation of the fuzzy rules is obtained form a stochastic dynamic programming (SDP) model with a steady state policy. The proposed model is applied to the hydropower operation of ''Dez'' reservoir in Iran and the results are presented and compared with those of the SDP model. The results indicate the ability of the method to solve hydropower reservoir operation problems. (author)

  6. A fuzzy Hopfield neural network for medical image segmentation

    International Nuclear Information System (INIS)

    Lin, J.S.; Cheng, K.S.; Mao, C.W.

    1996-01-01

    In this paper, an unsupervised parallel segmentation approach using a fuzzy Hopfield neural network (FHNN) is proposed. The main purpose is to embed fuzzy clustering into neural networks so that on-line learning and parallel implementation for medical image segmentation are feasible. The idea is to cast a clustering problem as a minimization problem where the criteria for the optimum segmentation is chosen as the minimization of the Euclidean distance between samples to class centers. In order to generate feasible results, a fuzzy c-means clustering strategy is included in the Hopfield neural network to eliminate the need of finding weighting factors in the energy function, which is formulated and based on a basic concept commonly used in pattern classification, called the within-class scatter matrix principle. The suggested fuzzy c-means clustering strategy has also been proven to be convergent and to allow the network to learn more effectively than the conventional Hopfield neural network. The fuzzy Hopfield neural network based on the within-class scatter matrix shows the promising results in comparison with the hard c-means method

  7. An adaptive neuro fuzzy model for estimating the reliability of component-based software systems

    Directory of Open Access Journals (Sweden)

    Kirti Tyagi

    2014-01-01

    Full Text Available Although many algorithms and techniques have been developed for estimating the reliability of component-based software systems (CBSSs, much more research is needed. Accurate estimation of the reliability of a CBSS is difficult because it depends on two factors: component reliability and glue code reliability. Moreover, reliability is a real-world phenomenon with many associated real-time problems. Soft computing techniques can help to solve problems whose solutions are uncertain or unpredictable. A number of soft computing approaches for estimating CBSS reliability have been proposed. These techniques learn from the past and capture existing patterns in data. The two basic elements of soft computing are neural networks and fuzzy logic. In this paper, we propose a model for estimating CBSS reliability, known as an adaptive neuro fuzzy inference system (ANFIS, that is based on these two basic elements of soft computing, and we compare its performance with that of a plain FIS (fuzzy inference system for different data sets.

  8. Online reinforcement learning control for aerospace systems

    NARCIS (Netherlands)

    Zhou, Y.

    2018-01-01

    Reinforcement Learning (RL) methods are relatively new in the field of aerospace guidance, navigation, and control. This dissertation aims to exploit RL methods to improve the autonomy and online learning of aerospace systems with respect to the a priori unknown system and environment, dynamical

  9. Statistical and Detailed Analysis on Fiber Reinforced Self-Compacting Concrete Containing Admixtures- A State of Art of Review

    Science.gov (United States)

    Athiyamaan, V.; Mohan Ganesh, G.

    2017-11-01

    Self-Compacting Concrete is one of the special concretes that have ability to flow and consolidate on its own weight, completely fill the formwork even in the presence of dense reinforcement; whilst maintaining its homogeneity throughout the formwork without any requirement for vibration. Researchers all over the world are developing high performance concrete by adding various Fibers, admixtures in different proportions. Various different kinds Fibers like glass, steel, carbon, Poly propylene and aramid Fibers provide improvement in concrete properties like tensile strength, fatigue characteristic, durability, shrinkage, impact, erosion resistance and serviceability of concrete[6]. It includes fundamental study on fiber reinforced self-compacting concrete with admixtures; its rheological properties, mechanical properties and overview study on design methodology statistical approaches regarding optimizing the concrete performances. The study has been classified into seven basic chapters: introduction, phenomenal study on material properties review on self-compacting concrete, overview on fiber reinforced self-compacting concrete containing admixtures, review on design and analysis of experiment; a statistical approach, summary of existing works on FRSCC and statistical modeling, literature review and, conclusion. It is so eminent to know the resent studies that had been done on polymer based binder materials (fly ash, metakaolin, GGBS, etc.), fiber reinforced concrete and SCC; to do an effective research on fiber reinforced self-compacting concrete containing admixtures. The key aim of the study is to sort-out the research gap and to gain a complete knowledge on polymer based Self compacting fiber reinforced concrete.

  10. PENINGKATAN SELF-REGULATED LEARNING SKILLS MAHASISWA PADA MATA KULIAH AKUNTANSI PENGANTAR MELALUI PROBLEM-BASED LEARNING

    Directory of Open Access Journals (Sweden)

    Andian Ari Istiningrum

    2017-02-01

    Full Text Available Abstrak: Penelitian ini bertujuan untuk mengetahui: (i peningkatan self-regulated learning skills (SRL melalui implementasi problem-based learning (PBL dan (ii peningkatan kemampuan dosen pelaksana dalam mengimplementasikan PBL. Penelitian ini merupakan lesson study terbagi atas dua siklus dimana masing-masing siklus terdiri atas tahap plan, do, dan see.Subjek penelitian adalah mahasiswa Akuntansi Universitas Negeri Yogyakarta semester pertama yang mengambil mata kuliah Akuntansi Pengantar sebanyak 35 mahasiswa. Data mengenai SRL dikumpulkan dengan angket yang diisi mahasiswa, sedangkan data mengenai implementasi PBL oleh dosen pelaksana dikumpulkan dengan lembar observasi yang diisi oleh mahasiswa dan anggota timlesson study. Data dianalisis secara deskriptif kualitatif dan kuantitatif. Hasil penelitian menunjukkan bahwa (i PBL mampu meningkatkan SRL mahasiswa walaupun tingkat ketercapaiannya masih belum optimal, dan (ii kemampuan dosen pelaksana dalam melaksanakan PBL meningkat dengan tingkat ketercapaian yang optimal. IMPROVING STUDENTS’ SELF-REGULATED LEARNING SKILLS IN THE INTRODUCTION TO ACCOUNTING COURSE THROUGH PROBLEM-BASED LEARNING Abstract: This study aims to reveal (i the improvement of self-regulated learning skills (SRL through problem-based learning (PBL, and (ii the improvement of lecturers’ performance in implementing PBL. To achieve these purposes, a lesson study with two cycles was conducted. Each cycle consisted of plan phase, do phase, and see phase. The study was conducted to the 1 semester Accounting Students at Yogyakarta State University who attended the Introduction to Accounting course. There were 35 students as the research subjects. The sampling technique used to collect data regarding SRL was questionnaires which were filled out by the students; while the data regarding the lecturer’s performance was collected by observation sheets that were filled out by students and members of lesson study group. The study

  11. Multi-agent machine learning a reinforcement approach

    CERN Document Server

    Schwartz, H M

    2014-01-01

    The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces. Chapter 3 discusses two player games including two player matrix games with both pure and mixed strategies. Numerous algorithms and examples are presented. Chapter 4 covers learning in multi-player games, stochastic games, and Markov games, focusing on learning multi-pla

  12. Design of interpretable fuzzy systems

    CERN Document Server

    Cpałka, Krzysztof

    2017-01-01

    This book shows that the term “interpretability” goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.

  13. Joy, Distress, Hope, and Fear in Reinforcement Learning (Extended Abstract)

    NARCIS (Netherlands)

    Jacobs, E.J.; Broekens, J.; Jonker, C.M.

    2014-01-01

    In this paper we present a mapping between joy, distress, hope and fear, and Reinforcement Learning primitives. Joy / distress is a signal that is derived from the RL update signal, while hope/fear is derived from the utility of the current state. Agent-based simulation experiments replicate

  14. Recognition of Handwritten Arabic words using a neuro-fuzzy network

    International Nuclear Information System (INIS)

    Boukharouba, Abdelhak; Bennia, Abdelhak

    2008-01-01

    We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system.The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database

  15. Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Maria Drakaki

    2017-02-01

    Full Text Available Agent-based intelligent manufacturing control systems are capable to efficiently respond and adapt to environmental changes. Manufacturing system adaptation and evolution can be addressed with learning mechanisms that increase the intelligence of agents. In this paper a manufacturing scheduling method is presented based on Timed Colored Petri Nets (CTPNs and reinforcement learning (RL. CTPNs model the manufacturing system and implement the scheduling. In the search for an optimal solution a scheduling agent uses RL and in particular the Q-learning algorithm. A warehouse order-picking scheduling is presented as a case study to illustrate the method. The proposed scheduling method is compared to existing methods. Simulation and state space results are used to evaluate performance and identify system properties.

  16. A TSK neuro-fuzzy approach for modeling highly dynamic systems

    NARCIS (Netherlands)

    Acampora, G.

    2011-01-01

    This paper introduces a new type of TSK-based neuro-fuzzy approach and its application to modeling highly dynamic systems. In details, our proposal performs an adaptive supervised learning on a collection of time series in order to create a so-called Timed Automata Based Fuzzy Controller, i.e. an

  17. Ensemble Network Architecture for Deep Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Xi-liang Chen

    2018-01-01

    Full Text Available The popular deep Q learning algorithm is known to be instability because of the Q-value’s shake and overestimation action values under certain conditions. These issues tend to adversely affect their performance. In this paper, we develop the ensemble network architecture for deep reinforcement learning which is based on value function approximation. The temporal ensemble stabilizes the training process by reducing the variance of target approximation error and the ensemble of target values reduces the overestimate and makes better performance by estimating more accurate Q-value. Our results show that this architecture leads to statistically significant better value evaluation and more stable and better performance on several classical control tasks at OpenAI Gym environment.

  18. Train Repathing in Emergencies Based on Fuzzy Linear Programming

    Directory of Open Access Journals (Sweden)

    Xuelei Meng

    2014-01-01

    Full Text Available Train pathing is a typical problem which is to assign the train trips on the sets of rail segments, such as rail tracks and links. This paper focuses on the train pathing problem, determining the paths of the train trips in emergencies. We analyze the influencing factors of train pathing, such as transferring cost, running cost, and social adverse effect cost. With the overall consideration of the segment and station capability constraints, we build the fuzzy linear programming model to solve the train pathing problem. We design the fuzzy membership function to describe the fuzzy coefficients. Furthermore, the contraction-expansion factors are introduced to contract or expand the value ranges of the fuzzy coefficients, coping with the uncertainty of the value range of the fuzzy coefficients. We propose a method based on triangular fuzzy coefficient and transfer the train pathing (fuzzy linear programming model to a determinate linear model to solve the fuzzy linear programming problem. An emergency is supposed based on the real data of the Beijing-Shanghai Railway. The model in this paper was solved and the computation results prove the availability of the model and efficiency of the algorithm.

  19. Train repathing in emergencies based on fuzzy linear programming.

    Science.gov (United States)

    Meng, Xuelei; Cui, Bingmou

    2014-01-01

    Train pathing is a typical problem which is to assign the train trips on the sets of rail segments, such as rail tracks and links. This paper focuses on the train pathing problem, determining the paths of the train trips in emergencies. We analyze the influencing factors of train pathing, such as transferring cost, running cost, and social adverse effect cost. With the overall consideration of the segment and station capability constraints, we build the fuzzy linear programming model to solve the train pathing problem. We design the fuzzy membership function to describe the fuzzy coefficients. Furthermore, the contraction-expansion factors are introduced to contract or expand the value ranges of the fuzzy coefficients, coping with the uncertainty of the value range of the fuzzy coefficients. We propose a method based on triangular fuzzy coefficient and transfer the train pathing (fuzzy linear programming model) to a determinate linear model to solve the fuzzy linear programming problem. An emergency is supposed based on the real data of the Beijing-Shanghai Railway. The model in this paper was solved and the computation results prove the availability of the model and efficiency of the algorithm.

  20. Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning

    OpenAIRE

    Yue Hu; Weimin Li; Kun Xu; Taimoor Zahid; Feiyan Qin; Chenming Li

    2018-01-01

    An energy management strategy (EMS) is important for hybrid electric vehicles (HEVs) since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions online. Therefore, it is useful that the EMS can learn from the environment or driving cycle. In this paper, a deep reinforcement learn...

  1. "Notice of Violation of IEEE Publication Principles" Multiobjective Reinforcement Learning: A Comprehensive Overview.

    Science.gov (United States)

    Liu, Chunming; Xu, Xin; Hu, Dewen

    2013-04-29

    Reinforcement learning is a powerful mechanism for enabling agents to learn in an unknown environment, and most reinforcement learning algorithms aim to maximize some numerical value, which represents only one long-term objective. However, multiple long-term objectives are exhibited in many real-world decision and control problems; therefore, recently, there has been growing interest in solving multiobjective reinforcement learning (MORL) problems with multiple conflicting objectives. The aim of this paper is to present a comprehensive overview of MORL. In this paper, the basic architecture, research topics, and naive solutions of MORL are introduced at first. Then, several representative MORL approaches and some important directions of recent research are reviewed. The relationships between MORL and other related research are also discussed, which include multiobjective optimization, hierarchical reinforcement learning, and multi-agent reinforcement learning. Finally, research challenges and open problems of MORL techniques are highlighted.

  2. Comparing Problem-Based Learning Students to Students in a Lecture-Based Curriculum: Learning Strategies and the Relation with Self-Study Time

    Science.gov (United States)

    Wijnen, Marit; Loyens, Sofie M. M.; Smeets, Guus; Kroeze, Maarten; van der Molen, Henk

    2017-01-01

    In educational theory, deep processing (i.e., connecting different study topics together) and self-regulation (i.e., taking control over one's own learning process) are considered effective learning strategies. These learning strategies can be influenced by the learning environment. Problem-based learning (PBL), a student-centered educational…

  3. Using Negative Reinforcement to Increase Self-Feeding in a Child with Food Selectivity

    Science.gov (United States)

    Vaz, Petula C. M.; Volkert, Valerie M.; Piazza, Cathleen C.

    2011-01-01

    We examined the effects of a negative reinforcement-based treatment on the self-feeding of 1 child with food selectivity by type and texture. Self-feeding increased when the child could choose to either self-feed 1 bite of a target food or be fed 1 bite of the target food and 5 bites of another food. Possible mechanisms that underlie the…

  4. A Fuzzy Rule-Based Expert System for Evaluating Intellectual Capital

    Directory of Open Access Journals (Sweden)

    Mohammad Hossein Fazel Zarandi

    2012-01-01

    Full Text Available A fuzzy rule-based expert system is developed for evaluating intellectual capital. A fuzzy linguistic approach assists managers to understand and evaluate the level of each intellectual capital item. The proposed fuzzy rule-based expert system applies fuzzy linguistic variables to express the level of qualitative evaluation and criteria of experts. Feasibility of the proposed model is demonstrated by the result of intellectual capital performance evaluation for a sample company.

  5. Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing

    OpenAIRE

    Le, Minh; Fokkens, Antske

    2017-01-01

    Error propagation is a common problem in NLP. Reinforcement learning explores erroneous states during training and can therefore be more robust when mistakes are made early in a process. In this paper, we apply reinforcement learning to greedy dependency parsing which is known to suffer from error propagation. Reinforcement learning improves accuracy of both labeled and unlabeled dependencies of the Stanford Neural Dependency Parser, a high performance greedy parser, while maintaining its eff...

  6. Chebyshev polynomial functions based locally recurrent neuro-fuzzy information system for prediction of financial and energy market data

    Directory of Open Access Journals (Sweden)

    A.K. Parida

    2016-09-01

    Full Text Available In this paper Chebyshev polynomial functions based locally recurrent neuro-fuzzy information system is presented for the prediction and analysis of financial and electrical energy market data. The normally used TSK-type feedforward fuzzy neural network is unable to take the full advantage of the use of the linear fuzzy rule base in accurate input–output mapping and hence the consequent part of the rule base is made nonlinear using polynomial or arithmetic basis functions. Further the Chebyshev polynomial functions provide an expanded nonlinear transformation to the input space thereby increasing its dimension for capturing the nonlinearities and chaotic variations in financial or energy market data streams. Also the locally recurrent neuro-fuzzy information system (LRNFIS includes feedback loops both at the firing strength layer and the output layer to allow signal flow both in forward and backward directions, thereby making the LRNFIS mimic a dynamic system that provides fast convergence and accuracy in predicting time series fluctuations. Instead of using forward and backward least mean square (FBLMS learning algorithm, an improved Firefly-Harmony search (IFFHS learning algorithm is used to estimate the parameters of the consequent part and feedback loop parameters for better stability and convergence. Several real world financial and energy market time series databases are used for performance validation of the proposed LRNFIS model.

  7. Self-tuning fuzzy logic nuclear reactor controller[Proceedings of the 2nd International FLINS Workshop (Mol, Belgium, September 25-27, 1996)

    Energy Technology Data Exchange (ETDEWEB)

    Sharif Heger, A; Alang-Rashid, N K

    1996-07-01

    We present a method for self-tuning of fuzzy logic controllers based on the estimation of the optimum value of the centroids of its output fuzzy set. The method can be implemented on-line and does not require modification of membership functions and control rules. The main features of this method are: the rules are left intact to retain the operator's expertise in the FLC rule base, and the parameters that require any adjustment are identifiable in advance and their number is kept at a minimum. Therefore, the use of this method preserves the control statements in the original form. Results of simulation and actual tests show that this tuning method improves the performance of fuzzy logic controllers in following the desired reactor power level trajectories. In addition, this method demonstrates a similar improvement for power up and power down experiments, based on both simulation and actual case studies. For these experiments, the control rules for the fuzzy logic controller were derived from control statements that expressed the relationships between error, rate of error change, and duration of direction of control rod movements.

  8. Self-Powered Wireless Sensor Network for Automated Corrosion Prediction of Steel Reinforcement

    Directory of Open Access Journals (Sweden)

    Dan Su

    2018-01-01

    Full Text Available Corrosion is one of the key issues that affect the service life and hinders wide application of steel reinforcement. Moreover, corrosion is a long-term process and not visible for embedded reinforcement. Thus, this research aims at developing a self-powered smart sensor system with integrated innovative prediction module for forecasting corrosion process of embedded steel reinforcement. Vibration-based energy harvester is used to harvest energy for continuous corrosion data collection. Spatial interpolation module was developed to interpolate corrosion data at unmonitored locations. Dynamic prediction module is used to predict the long-term corrosion based on collected data. Utilizing this new sensor network, the corrosion process can be automated predicted and appropriate mitigation actions will be recommended accordingly.

  9. Accurate crop classification using hierarchical genetic fuzzy rule-based systems

    Science.gov (United States)

    Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.

    2014-10-01

    This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

  10. A Fuzzy Neural Network Based on Non-Euclidean Distance Clustering for Quality Index Model in Slashing Process

    Directory of Open Access Journals (Sweden)

    Yuxian Zhang

    2015-01-01

    Full Text Available The quality index model in slashing process is difficult to build by reason of the outliers and noise data from original data. To the above problem, a fuzzy neural network based on non-Euclidean distance clustering is proposed in which the input space is partitioned into many local regions by the fuzzy clustering based on non-Euclidean distance so that the computation complexity is decreased, and fuzzy rule number is determined by validity function based on both the separation and the compactness among clusterings. Then, the premise parameters and consequent parameters are trained by hybrid learning algorithm. The parameters identification is realized; meanwhile the convergence condition of consequent parameters is obtained by Lyapunov function. Finally, the proposed method is applied to build the quality index model in slashing process in which the experimental data come from the actual slashing process. The experiment results show that the proposed fuzzy neural network for quality index model has lower computation complexity and faster convergence time, comparing with GP-FNN, BPNN, and RBFNN.

  11. New Algorithm for the Smoothing Speed Control of Induction Motor in Electric Car based on Self-Tuning Parameter PID-Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Dedid Cahya Happyanto

    2012-05-01

    Full Text Available Driving system of electric car for low speed has a performance of controller that is not easily set up on large span so it does not give a comfort to passengers. The study has been tested in the bumpy road conditions, by providing disturbances in the motor load, it is to describe the condition of the road. To improve the system performance, the speed and torque controller was applied using Field Oriented Control (FOC method. In this method, On-Line Proportional Integral Derivative Fuzzy Logic Controller (PID-FLC is used to give dynamic response to the change of speed and maximum torque on the electric car and this results the smooth movement on every change of car performance both in fast and slow movement when breaking action is taken. Optimization of membership functions in Fuzzy PID controller is required to obtain a new PID parameter values which is done in autotuning in any changes of the input or disturbance. PID parameter tuning in this case using the Ziegler-Nichols method based on frequency response. The mechanism is done by adjusting the PID parameters and the strengthening of the system output. The test results show that the controller Fuzzy Self-Tuning PID appropriate for Electric cars because they have a good response about 0.85% overshoot at to changes in speed and braking of electric cars.

  12. Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning.

    Science.gov (United States)

    Pilarski, Patrick M; Dawson, Michael R; Degris, Thomas; Fahimi, Farbod; Carey, Jason P; Sutton, Richard S

    2011-01-01

    As a contribution toward the goal of adaptable, intelligent artificial limbs, this work introduces a continuous actor-critic reinforcement learning method for optimizing the control of multi-function myoelectric devices. Using a simulated upper-arm robotic prosthesis, we demonstrate how it is possible to derive successful limb controllers from myoelectric data using only a sparse human-delivered training signal, without requiring detailed knowledge about the task domain. This reinforcement-based machine learning framework is well suited for use by both patients and clinical staff, and may be easily adapted to different application domains and the needs of individual amputees. To our knowledge, this is the first my-oelectric control approach that facilitates the online learning of new amputee-specific motions based only on a one-dimensional (scalar) feedback signal provided by the user of the prosthesis. © 2011 IEEE

  13. Advance study of fiber-reinforced self-compacting concrete

    International Nuclear Information System (INIS)

    Mironova, M.; Ivanova, M.; Naidenov, V.; Georgiev, I.; Stary, J.

    2015-01-01

    Incorporation in concrete composition of steel macro- and micro – fiber reinforcement with structural function increases the degree of ductility of typically brittle cement-containing composites, which in some cases can replace completely or partially conventional steel reinforcement in the form of rods and meshes. Thus, that can reduce manufacturing, detailing and placement of conventional reinforcement, which enhances productivity and economic efficiency of the building process. In this paper, six fiber-reinforced with different amounts of steel fiber cement-containing self-compacting compositions are investigated. The results of some of their main strength-deformation characteristics are presented. Advance approach for the study of structural and material properties of these type composites is proposed by using the methods of industrial computed tomography. The obtained original tomography results about the microstructure and characteristics of individual structural components make it possible to analyze the effective macro-characteristics of the studied composites. The resulting analytical data are relevant for the purposes of multi-dimensional modeling of these systems. Multifactor structure-mechanical analysis of the obtained with different methods original scientific results is proposed. It is presented a conclusion of the capabilities and effectiveness of complex analysis in the studies to characterize the properties of self-compacting fiber-reinforced concrete

  14. Advance study of fiber-reinforced self-compacting concrete

    Science.gov (United States)

    Mironova, M.; Ivanova, M.; Naidenov, V.; Georgiev, I.; Stary, J.

    2015-10-01

    Incorporation in concrete composition of steel macro- and micro - fiber reinforcement with structural function increases the degree of ductility of typically brittle cement-containing composites, which in some cases can replace completely or partially conventional steel reinforcement in the form of rods and meshes. Thus, that can reduce manufacturing, detailing and placement of conventional reinforcement, which enhances productivity and economic efficiency of the building process. In this paper, six fiber-reinforced with different amounts of steel fiber cement-containing self-compacting compositions are investigated. The results of some of their main strength-deformation characteristics are presented. Advance approach for the study of structural and material properties of these type composites is proposed by using the methods of industrial computed tomography. The obtained original tomography results about the microstructure and characteristics of individual structural components make it possible to analyze the effective macro-characteristics of the studied composites. The resulting analytical data are relevant for the purposes of multi-dimensional modeling of these systems. Multifactor structure-mechanical analysis of the obtained with different methods original scientific results is proposed. It is presented a conclusion of the capabilities and effectiveness of complex analysis in the studies to characterize the properties of self-compacting fiber-reinforced concrete.

  15. Advance study of fiber-reinforced self-compacting concrete

    Energy Technology Data Exchange (ETDEWEB)

    Mironova, M., E-mail: mirona@imbm.bas.bg; Ivanova, M., E-mail: magdalena.ivanova@imbm.bas.bg; Naidenov, V., E-mail: valna53@mail.bg [Institute of Mechanics, Bulgarian Academy of Sciences, Acad. G. Bonchev str., bl. 4, Sofia 1113 (Bulgaria); Georgiev, I., E-mail: ivan.georgiev@parallel.bas.bg [Institute of Information and Communication Technologies & Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Acad. G. Bonchev str., Sofia 1113 (Bulgaria); Stary, J., E-mail: stary@ugn.cas.cz [Institute of Geonics Czech Academy of Sciences, Studentska str., Ostrava 1768 (Czech Republic)

    2015-10-28

    Incorporation in concrete composition of steel macro- and micro – fiber reinforcement with structural function increases the degree of ductility of typically brittle cement-containing composites, which in some cases can replace completely or partially conventional steel reinforcement in the form of rods and meshes. Thus, that can reduce manufacturing, detailing and placement of conventional reinforcement, which enhances productivity and economic efficiency of the building process. In this paper, six fiber-reinforced with different amounts of steel fiber cement-containing self-compacting compositions are investigated. The results of some of their main strength-deformation characteristics are presented. Advance approach for the study of structural and material properties of these type composites is proposed by using the methods of industrial computed tomography. The obtained original tomography results about the microstructure and characteristics of individual structural components make it possible to analyze the effective macro-characteristics of the studied composites. The resulting analytical data are relevant for the purposes of multi-dimensional modeling of these systems. Multifactor structure-mechanical analysis of the obtained with different methods original scientific results is proposed. It is presented a conclusion of the capabilities and effectiveness of complex analysis in the studies to characterize the properties of self-compacting fiber-reinforced concrete.

  16. Self-compacting fibre reinforced concrete applied in thin plates

    NARCIS (Netherlands)

    Grunewald, S.; Shionaga, R.; Walraven, J.C.

    2013-01-01

    Floor panels produced with traditionally vibrated concrete are relatively thick due to the need to reinforce concrete and consequently, heavy. Without the need to place rebars in panels and by applying self-compacting fibre reinforced concrete (SCFRC) the production process becomes more efficient.

  17. Longitudinal investigation on learned helplessness tested under negative and positive reinforcement involving stimulus control.

    Science.gov (United States)

    Oliveira, Emileane C; Hunziker, Maria Helena

    2014-07-01

    In this study, we investigated whether (a) animals demonstrating the learned helplessness effect during an escape contingency also show learning deficits under positive reinforcement contingencies involving stimulus control and (b) the exposure to positive reinforcement contingencies eliminates the learned helplessness effect under an escape contingency. Rats were initially exposed to controllable (C), uncontrollable (U) or no (N) shocks. After 24h, they were exposed to 60 escapable shocks delivered in a shuttlebox. In the following phase, we selected from each group the four subjects that presented the most typical group pattern: no escape learning (learned helplessness effect) in Group U and escape learning in Groups C and N. All subjects were then exposed to two phases, the (1) positive reinforcement for lever pressing under a multiple FR/Extinction schedule and (2) a re-test under negative reinforcement (escape). A fourth group (n=4) was exposed only to the positive reinforcement sessions. All subjects showed discrimination learning under multiple schedule. In the escape re-test, the learned helplessness effect was maintained for three of the animals in Group U. These results suggest that the learned helplessness effect did not extend to discriminative behavior that is positively reinforced and that the learned helplessness effect did not revert for most subjects after exposure to positive reinforcement. We discuss some theoretical implications as related to learned helplessness as an effect restricted to aversive contingencies and to the absence of reversion after positive reinforcement. This article is part of a Special Issue entitled: insert SI title. Copyright © 2014. Published by Elsevier B.V.

  18. The role of multiple neuromodulators in reinforcement learning that is based on competition between eligibility traces

    Directory of Open Access Journals (Sweden)

    Marco A Huertas

    2016-12-01

    Full Text Available The ability to maximize reward and avoid punishment is essential for animal survival. Reinforcement learning (RL refers to the algorithms used by biological or artificial systems to learn how to maximize reward or avoid negative outcomes based on past experiences. While RL is also important in machine learning, the types of mechanistic constraints encountered by biological machinery might be different than those for artificial systems. Two major problems encountered by RL are how to relate a stimulus with a reinforcing signal that is delayed in time (temporal credit assignment, and how to stop learning once the target behaviors are attained (stopping rule. To address the first problem, synaptic eligibility traces were introduced, bridging the temporal gap between a stimulus and its reward. Although these were mere theoretical constructs, recent experiements have provided evidence of their existence. These experiments also reveal that the presence of specific neuromodulators converts the traces into changes in synaptic efficacy. A mechanistic implementation of the stopping rule usually assumes the inhibition of the reward nucleus; however, recent experimental results have shown that learning terminates at the appropriate network state even in setups where the reward cannot be inhibited. In an effort to describe a learning rule that solves the temporal credit assignment problem and implements a biologically plausible stopping rule, we proposed a model based on two separate synaptic eligibility traces, one for long-term potentiation (LTP and one for long-term depression (LTD, each obeying different dynamics and having different effective magnitudes. The model has been shown to successfully generate stable learning in recurrent networks. Although the model assumes the presence of a single neuromodulator, evidence indicates that there are different neuromodulators for expressing the different traces. What could be the role of different

  19. The Role of Multiple Neuromodulators in Reinforcement Learning That Is Based on Competition between Eligibility Traces.

    Science.gov (United States)

    Huertas, Marco A; Schwettmann, Sarah E; Shouval, Harel Z

    2016-01-01

    The ability to maximize reward and avoid punishment is essential for animal survival. Reinforcement learning (RL) refers to the algorithms used by biological or artificial systems to learn how to maximize reward or avoid negative outcomes based on past experiences. While RL is also important in machine learning, the types of mechanistic constraints encountered by biological machinery might be different than those for artificial systems. Two major problems encountered by RL are how to relate a stimulus with a reinforcing signal that is delayed in time (temporal credit assignment), and how to stop learning once the target behaviors are attained (stopping rule). To address the first problem synaptic eligibility traces were introduced, bridging the temporal gap between a stimulus and its reward. Although, these were mere theoretical constructs, recent experiments have provided evidence of their existence. These experiments also reveal that the presence of specific neuromodulators converts the traces into changes in synaptic efficacy. A mechanistic implementation of the stopping rule usually assumes the inhibition of the reward nucleus; however, recent experimental results have shown that learning terminates at the appropriate network state even in setups where the reward nucleus cannot be inhibited. In an effort to describe a learning rule that solves the temporal credit assignment problem and implements a biologically plausible stopping rule, we proposed a model based on two separate synaptic eligibility traces, one for long-term potentiation (LTP) and one for long-term depression (LTD), each obeying different dynamics and having different effective magnitudes. The model has been shown to successfully generate stable learning in recurrent networks. Although, the model assumes the presence of a single neuromodulator, evidence indicates that there are different neuromodulators for expressing the different traces. What could be the role of different neuromodulators for

  20. Optimal and Autonomous Control Using Reinforcement Learning: A Survey.

    Science.gov (United States)

    Kiumarsi, Bahare; Vamvoudakis, Kyriakos G; Modares, Hamidreza; Lewis, Frank L

    2018-06-01

    This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal and control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.

  1. Fuzzy knowledge base construction through belief networks based on Lukasiewicz logic

    Science.gov (United States)

    Lara-Rosano, Felipe

    1992-01-01

    In this paper, a procedure is proposed to build a fuzzy knowledge base founded on fuzzy belief networks and Lukasiewicz logic. Fuzzy procedures are developed to do the following: to assess the belief values of a consequent, in terms of the belief values of its logical antecedents and the belief value of the corresponding logical function; and to update belief values when new evidence is available.

  2. Enhancing students' learning in problem based learning: validation of a self-assessment scale for active learning and critical thinking

    NARCIS (Netherlands)

    Khoiriyah, U.; Roberts, C.; Jorm, C.; Vleuten, C.P. van der

    2015-01-01

    BACKGROUND: Problem based learning (PBL) is a powerful learning activity but fidelity to intended models may slip and student engagement wane, negatively impacting learning processes, and outcomes. One potential solution to solve this degradation is by encouraging self-assessment in the PBL

  3. Fuzzy preference based interactive fuzzy physical programming and its application in multi-objective optimization

    International Nuclear Information System (INIS)

    Zhang, Xu; Huang, Hong Zhong; Yu, Lanfeng

    2006-01-01

    Interactive Fuzzy Physical Programming (IFPP) developed in this paper is a new efficient multi-objective optimization method, which retains the advantages of physical programming while considering the fuzziness of the designer's preferences. The fuzzy preference function is introduced based on the model of linear physical programming, which is used to guide the search for improved solutions by interactive decision analysis. The example of multi-objective optimization design of the spindle of internal grinder demonstrates that the improved preference conforms to the subjective desires of the designer

  4. A Fuzzy Control Course on the TED Server

    DEFF Research Database (Denmark)

    Dotoli, Mariagrazia; Jantzen, Jan

    1999-01-01

    , an educational server that serves as a learning central for students and professionals working with fuzzy logic. Through the server, TED offers an online course on fuzzy control. The course concerns automatic control of an inverted pendulum, with a focus on rule based control by means of fuzzy logic. A ball......The Training and Education Committee (TED) is a committee under ERUDIT, a Network of Excellence for fuzzy technology and uncertainty in Europe. The main objective of TED is to improve the training and educational possibilities for the nodes of ERUDIT. Since early 1999, TED has set up the TED server...

  5. Investigation of a Reinforcement-Based Toilet Training Procedure for Children with Autism.

    Science.gov (United States)

    Cicero, Frank R.; Pfadt, Al

    2002-01-01

    This study evaluated the effectiveness of a reinforcement-based toilet training intervention with three children with autism. Procedures included positive reinforcement, graduated guidance, scheduled practice trials, and forward prompting. All three children reduced urination accidents to zero and learned to request bathroom use spontaneously…

  6. A new fuzzy regression model based on interval-valued fuzzy neural network and its applications to management

    Directory of Open Access Journals (Sweden)

    Somaye Yeylaghi

    2017-06-01

    Full Text Available In this paper, a novel hybrid method based on interval-valued fuzzy neural network for approximate of interval-valued fuzzy regression models, is presented. The work of this paper is an expansion of the research of real fuzzy regression models. In this paper interval-valued fuzzy neural network (IVFNN can be trained with crisp and interval-valued fuzzy data. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples and compare this method with existing methods.

  7. Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation

    Science.gov (United States)

    Ibrahim, Wael Refaat Anis

    The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an

  8. Web-Based Reading Annotation System with an Attention-Based Self-Regulated Learning Mechanism for Promoting Reading Performance

    Science.gov (United States)

    Chen, Chih-Ming; Huang, Sheng-Hui

    2014-01-01

    Due to the rapid development of information technology, web-based learning has become a dominant trend. That is, learners can often learn anytime and anywhere without being restricted by time and space. Autonomic learning primarily occurs in web-based learning environments, and self-regulated learning (SRL) is key to autonomic learning…

  9. Adaptive fuzzy controller based MPPT for photovoltaic systems

    International Nuclear Information System (INIS)

    Guenounou, Ouahib; Dahhou, Boutaib; Chabour, Ferhat

    2014-01-01

    Highlights: • We propose a fuzzy controller with adaptive output scaling factor as a maximum power point tracker of photovoltaic system. • The proposed controller integrates two different rule bases defined on error and change of error. • Our controller can track the maximum power point with better performances when compared to its conventional counterpart. - Abstract: This paper presents an intelligent approach to optimize the performances of photovoltaic systems. The system consists of a PV panel, a DC–DC boost converter, a maximum power point tracker controller and a resistive load. The key idea of the proposed approach is the use of a fuzzy controller with an adaptive gain as a maximum power point tracker. The proposed controller integrates two different rule bases. The first is used to adjust the duty cycle of the boost converter as in the case of a conventional fuzzy controller while the second rule base is designed for an online adjusting of the controller’s gain. The performances of the adaptive fuzzy controller are compared with those obtained using a conventional fuzzy controllers with different gains and in each case, the proposed controller outperforms its conventional counterpart

  10. Combining Correlation-Based and Reward-Based Learning in Neural Control for Policy Improvement

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Kolodziejski, Christoph; Wörgötter, Florentin

    2013-01-01

    Classical conditioning (conventionally modeled as correlation-based learning) and operant conditioning (conventionally modeled as reinforcement learning or reward-based learning) have been found in biological systems. Evidence shows that these two mechanisms strongly involve learning about...... associations. Based on these biological findings, we propose a new learning model to achieve successful control policies for artificial systems. This model combines correlation-based learning using input correlation learning (ICO learning) and reward-based learning using continuous actor–critic reinforcement...... learning (RL), thereby working as a dual learner system. The model performance is evaluated by simulations of a cart-pole system as a dynamic motion control problem and a mobile robot system as a goal-directed behavior control problem. Results show that the model can strongly improve pole balancing control...

  11. How we learn to make decisions: rapid propagation of reinforcement learning prediction errors in humans.

    Science.gov (United States)

    Krigolson, Olav E; Hassall, Cameron D; Handy, Todd C

    2014-03-01

    Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward

  12. The effectiveness of collaborative problem based physics learning (CPBPL) model to improve student’s self-confidence on physics learning

    Science.gov (United States)

    Prahani, B. K.; Suprapto, N.; Suliyanah; Lestari, N. A.; Jauhariyah, M. N. R.; Admoko, S.; Wahyuni, S.

    2018-03-01

    In the previous research, Collaborative Problem Based Physic Learning (CPBPL) model has been developed to improve student’s science process skills, collaborative problem solving, and self-confidence on physics learning. This research is aimed to analyze the effectiveness of CPBPL model towards the improvement of student’s self-confidence on physics learning. This research implemented quasi experimental design on 140 senior high school students who were divided into 4 groups. Data collection was conducted through questionnaire, observation, and interview. Self-confidence measurement was conducted through Self-Confidence Evaluation Sheet (SCES). The data was analyzed using Wilcoxon test, n-gain, and Kruskal Wallis test. Result shows that: (1) There is a significant score improvement on student’s self-confidence on physics learning (α=5%), (2) n-gain value student’s self-confidence on physics learning is high, and (3) n-gain average student’s self-confidence on physics learning was consistent throughout all groups. It can be concluded that CPBPL model is effective to improve student’s self-confidence on physics learning.

  13. Vision-based Navigation and Reinforcement Learning Path Finding for Social Robots

    OpenAIRE

    Pérez Sala, Xavier

    2010-01-01

    We propose a robust system for automatic Robot Navigation in uncontrolled en- vironments. The system is composed by three main modules: the Arti cial Vision module, the Reinforcement Learning module, and the behavior control module. The aim of the system is to allow a robot to automatically nd a path that arrives to a pre xed goal. Turn and straight movements in uncontrolled environments are automatically estimated and controlled using the proposed modules. The Arti cial Vi...

  14. Flow Navigation by Smart Microswimmers via Reinforcement Learning

    Science.gov (United States)

    Colabrese, Simona; Biferale, Luca; Celani, Antonio; Gustavsson, Kristian

    2017-11-01

    We have numerically modeled active particles which are able to acquire some limited knowledge of the fluid environment from simple mechanical cues and exert a control on their preferred steering direction. We show that those swimmers can learn effective strategies just by experience, using a reinforcement learning algorithm. As an example, we focus on smart gravitactic swimmers. These are active particles whose task is to reach the highest altitude within some time horizon, exploiting the underlying flow whenever possible. The reinforcement learning algorithm allows particles to learn effective strategies even in difficult situations when, in the absence of control, they would end up being trapped by flow structures. These strategies are highly nontrivial and cannot be easily guessed in advance. This work paves the way towards the engineering of smart microswimmers that solve difficult navigation problems. ERC AdG NewTURB 339032.

  15. A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system

    Energy Technology Data Exchange (ETDEWEB)

    Chaouachi, Aymen; Kamel, Rashad M.; Nagasaka, Ken [Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Nakamachi (Japan)

    2010-12-15

    This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P and O) algorithm dispositive. (author)

  16. Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning.

    Science.gov (United States)

    Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming

    2012-01-31

    Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

  17. The Effect of Multimedia Based Learning in Chemistry Teaching and Learning on Students’ Self-Regulated Learning

    Directory of Open Access Journals (Sweden)

    Erfan Priyambodo

    2014-11-01

    Full Text Available In recent years, the uses of Multimedia Based Learning (MBL in classroom instruction increased widely. Overall, this implementation aims to improve students’ motivation and also their learning outcomes. This study was answering the effect of MBL toward students’ Self-Regulated Learning (SRL in chemistry teaching and learning. The experiment was conducted in class XI of senior high school in Yogyakarta. Researchers create some computer based media for chemistry materials and continued with expert judgement of the media. Students’ data SRL were measured using validated questionnaire. The questionnaire consists of three components, i.e. metacognitive, motivation and behavior. The results showed that there was significant differences in SRL of students before and after participating in chemistry teaching and learning which applying MBL.

  18. A study on water level control of PWR steam generator at low power and the self-tuning of its fuzzy controller

    International Nuclear Information System (INIS)

    Na, N.; Kwon, K.; Ham, C.; Bien, Z.

    1994-01-01

    The water level control system of a steam generator in a pressurized water reactor and its control problems during the operation at low power is analysed. In particular, a strategy for a water level control system, which is based on the use of a fuzzy logic controller, is proposed. The control strategy includes dynamic tuning for the large transient. The fuzzy variable of the flow rate during the power operation is obtained from the bypass valve opening and not from the incorrect measured signal at the low flow rate. The practical self-tuning algorithm is based on the optimal control performance

  19. A fuzzy behaviorist approach to sensor-based robot control

    Energy Technology Data Exchange (ETDEWEB)

    Pin, F.G.

    1996-05-01

    Sensor-based operation of autonomous robots in unstructured and/or outdoor environments has revealed to be an extremely challenging problem, mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment, i.e., lack of full knowledge of the environment characteristics and dynamics. An approach. which we have named the {open_quotes}Fuzzy Behaviorist Approach{close_quotes} (FBA) is proposed in an attempt to remedy some of these difficulties. This approach is based on the representation of the system`s uncertainties using Fuzzy Set Theory-based approximations and on the representation of the reasoning and control schemes as sets of elemental behaviors. Using the FBA, a formalism for rule base development and an automated generator of fuzzy rules have been developed. This automated system can automatically construct the set of membership functions corresponding to fuzzy behaviors. Once these have been expressed in qualitative terms by the user. The system also checks for completeness of the rule base and for non-redundancy of the rules (which has traditionally been a major hurdle in rule base development). Two major conceptual features, the suppression and inhibition mechanisms which allow to express a dominance between behaviors are discussed in detail. Some experimental results obtained with the automated fuzzy, rule generator applied to the domain of sensor-based navigation in aprion unknown environments. using one of our autonomous test-bed robots as well as a real car in outdoor environments, are then reviewed and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using the {open_quotes}Fuzzy Behaviorist{close_quotes} concepts.

  20. Fuzzy-set based contingency ranking

    International Nuclear Information System (INIS)

    Hsu, Y.Y.; Kuo, H.C.

    1992-01-01

    In this paper, a new approach based on fuzzy set theory is developed for contingency ranking of Taiwan power system. To examine whether a power system can remain in a secure and reliable operating state under contingency conditions, those contingency cases that will result in loss-of-load, loss-of generation, or islanding are first identified. Then 1P-1Q iteration of fast decoupled load flow is preformed to estimate post-contingent quantities (line flows, bus voltages) for other contingency cases. Based on system operators' past experience, each post-contingent quantity is assigned a degree of severity according to the potential damage that could be imposed on the power system by the quantity, should the contingency occurs. An approach based on fuzzy set theory is developed to deal with the imprecision of linguistic terms

  1. Learning preferences from paired opposite-based semantics

    DEFF Research Database (Denmark)

    Franco de los Ríos, Camilo; Rodríguez, J. Tinguaro; Montero, Javier

    2017-01-01

    Preference semantics examine the meaning of the preference predicate, according to the way that alternatives can be understood and organized for decision making purposes. Through opposite-based semantics, preference structures can be characterized by their paired decomposition of preference...... on the character of opposition, the compound meaning of preference emerges from the fuzzy reinforcement of paired opposite concepts, searching for significant evidence for affirming dominance among the decision objects. Here we propose a general model for the paired decomposition of preference, examining its...

  2. Optimal control in microgrid using multi-agent reinforcement learning.

    Science.gov (United States)

    Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

    2012-11-01

    This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Fuzzy logic based control system for fresh water aquaculture: A MATLAB based simulation approach

    Directory of Open Access Journals (Sweden)

    Rana Dinesh Singh

    2015-01-01

    Full Text Available Fuzzy control is regarded as the most widely used application of fuzzy logic. Fuzzy logic is an innovative technology to design solutions for multiparameter and non-linear control problems. One of the greatest advantages of fuzzy control is that it uses human experience and process information obtained from operator rather than a mathematical model for the definition of a control strategy. As a result, it often delivers solutions faster than conventional control design techniques. The proposed system is an attempt to apply fuzzy logic techniques to predict the stress factor on the fish, based on line data and rule base generated using domain expert. The proposed work includes a use of Data acquisition system, an interfacing device for on line parameter acquisition and analysis, fuzzy logic controller (FLC for inferring the stress factor. The system takes stress parameters on the fish as inputs, fuzzified by using FLC with knowledge base rules and finally provides single output. All the parameters are controlled and calibrated by the fuzzy logic toolbox and MATLAB programming.

  4. Development of Interactive Media for ICT Learning at Elementary School Based on Student Self Learning

    Directory of Open Access Journals (Sweden)

    Sri Huning Anwariningsih

    2013-05-01

    Full Text Available The implementation of information and comunication technology (ICT curriculum at elementary school is the educational sector development. ICT subject is a practical subject which require a direct practice to make easier in the student understanding. Therefore, a teacher is demanded to make a learning media which helps the student to understand the material of study. This research is aimed at describing the model of ICT study in elementary school and using of learning media. Moreover, the description can be bocome one of the basic from the development of interactive study model base on student self learning. Besides, the arraging of this study model is hoped to make habitual and self learning.

  5. Enhancing students' learning in problem based learning: validation of a self-assessment scale for active learning and critical thinking.

    Science.gov (United States)

    Khoiriyah, Umatul; Roberts, Chris; Jorm, Christine; Van der Vleuten, C P M

    2015-08-26

    Problem based learning (PBL) is a powerful learning activity but fidelity to intended models may slip and student engagement wane, negatively impacting learning processes, and outcomes. One potential solution to solve this degradation is by encouraging self-assessment in the PBL tutorial. Self-assessment is a central component of the self-regulation of student learning behaviours. There are few measures to investigate self-assessment relevant to PBL processes. We developed a Self-assessment Scale on Active Learning and Critical Thinking (SSACT) to address this gap. We wished to demonstrated evidence of its validity in the context of PBL by exploring its internal structure. We used a mixed methods approach to scale development. We developed scale items from a qualitative investigation, literature review, and consideration of previous existing tools used for study of the PBL process. Expert review panels evaluated its content; a process of validation subsequently reduced the pool of items. We used structural equation modelling to undertake a confirmatory factor analysis (CFA) of the SSACT and coefficient alpha. The 14 item SSACT consisted of two domains "active learning" and "critical thinking." The factorial validity of SSACT was evidenced by all items loading significantly on their expected factors, a good model fit for the data, and good stability across two independent samples. Each subscale had good internal reliability (>0.8) and strongly correlated with each other. The SSACT has sufficient evidence of its validity to support its use in the PBL process to encourage students to self-assess. The implementation of the SSACT may assist students to improve the quality of their learning in achieving PBL goals such as critical thinking and self-directed learning.

  6. Transportation optimization with fuzzy trapezoidal numbers based on possibility theory.

    Science.gov (United States)

    He, Dayi; Li, Ran; Huang, Qi; Lei, Ping

    2014-01-01

    In this paper, a parametric method is introduced to solve fuzzy transportation problem. Considering that parameters of transportation problem have uncertainties, this paper develops a generalized fuzzy transportation problem with fuzzy supply, demand and cost. For simplicity, these parameters are assumed to be fuzzy trapezoidal numbers. Based on possibility theory and consistent with decision-makers' subjectiveness and practical requirements, the fuzzy transportation problem is transformed to a crisp linear transportation problem by defuzzifying fuzzy constraints and objectives with application of fractile and modality approach. Finally, a numerical example is provided to exemplify the application of fuzzy transportation programming and to verify the validity of the proposed methods.

  7. Embedded Incremental Feature Selection for Reinforcement Learning

    Science.gov (United States)

    2012-05-01

    Prior to this work, feature selection for reinforce- ment learning has focused on linear value function ap- proximation ( Kolter and Ng, 2009; Parr et al...InProceed- ings of the the 23rd International Conference on Ma- chine Learning, pages 449–456. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature

  8. Application of ANNs approach for solving fully fuzzy polynomials system

    Directory of Open Access Journals (Sweden)

    R. Novin

    2017-11-01

    Full Text Available In processing indecisive or unclear information, the advantages of fuzzy logic and neurocomputing disciplines should be taken into account and combined by fuzzy neural networks. The current research intends to present a fuzzy modeling method using multi-layer fuzzy neural networks for solving a fully fuzzy polynomials system. To clarify the point, it is necessary to inform that a supervised gradient descent-based learning law is employed. The feasibility of the method is examined using computer simulations on a numerical example. The experimental results obtained from the investigation of the proposed method are valid and delivers very good approximation results.

  9. A fuzzy art neural network based color image processing and ...

    African Journals Online (AJOL)

    To improve the learning process from the input data, a new learning rule was suggested. In this paper, a new method is proposed to deal with the RGB color image pixels, which enables a Fuzzy ART neural network to process the RGB color images. The application of the algorithm was implemented and tested on a set of ...

  10. Flexible Heuristic Dynamic Programming for Reinforcement Learning in Quadrotors

    NARCIS (Netherlands)

    Helmer, Alexander; de Visser, C.C.; van Kampen, E.

    2018-01-01

    Reinforcement learning is a paradigm for learning decision-making tasks from interaction with the environment. Function approximators solve a part of the curse of dimensionality when learning in high-dimensional state and/or action spaces. It can be a time-consuming process to learn a good policy in

  11. Novel Fuzzy-Modeling-Based Adaptive Synchronization of Nonlinear Dynamic Systems

    Directory of Open Access Journals (Sweden)

    Shih-Yu Li

    2017-01-01

    Full Text Available In this paper, a novel fuzzy-model-based adaptive synchronization scheme and its fuzzy update laws of parameters are proposed to address the adaptive synchronization problem. The proposed fuzzy controller does not share the same premise of fuzzy system, and the numbers of fuzzy controllers is reduced effectively through the novel modeling strategy. In addition, based on the adaptive synchronization scheme, the error dynamic system can be guaranteed to be asymptotically stable and the true values of unknown parameters can be obtained. Two identical complicated dynamic systems, Mathieu-Van der pol system (M-V system with uncertainties, are illustrated for numerical simulation example to show the effectiveness and feasibility of the proposed novel adaptive control strategy.

  12. Fuzzy probability based fault tree analysis to propagate and quantify epistemic uncertainty

    International Nuclear Information System (INIS)

    Purba, Julwan Hendry; Sony Tjahyani, D.T.; Ekariansyah, Andi Sofrany; Tjahjono, Hendro

    2015-01-01

    Highlights: • Fuzzy probability based fault tree analysis is to evaluate epistemic uncertainty in fuzzy fault tree analysis. • Fuzzy probabilities represent likelihood occurrences of all events in a fault tree. • A fuzzy multiplication rule quantifies epistemic uncertainty of minimal cut sets. • A fuzzy complement rule estimate epistemic uncertainty of the top event. • The proposed FPFTA has successfully evaluated the U.S. Combustion Engineering RPS. - Abstract: A number of fuzzy fault tree analysis approaches, which integrate fuzzy concepts into the quantitative phase of conventional fault tree analysis, have been proposed to study reliabilities of engineering systems. Those new approaches apply expert judgments to overcome the limitation of the conventional fault tree analysis when basic events do not have probability distributions. Since expert judgments might come with epistemic uncertainty, it is important to quantify the overall uncertainties of the fuzzy fault tree analysis. Monte Carlo simulation is commonly used to quantify the overall uncertainties of conventional fault tree analysis. However, since Monte Carlo simulation is based on probability distribution, this technique is not appropriate for fuzzy fault tree analysis, which is based on fuzzy probabilities. The objective of this study is to develop a fuzzy probability based fault tree analysis to overcome the limitation of fuzzy fault tree analysis. To demonstrate the applicability of the proposed approach, a case study is performed and its results are then compared to the results analyzed by a conventional fault tree analysis. The results confirm that the proposed fuzzy probability based fault tree analysis is feasible to propagate and quantify epistemic uncertainties in fault tree analysis

  13. Uncertain rule-based fuzzy systems introduction and new directions

    CERN Document Server

    Mendel, Jerry M

    2017-01-01

    The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems – from type-1 to interval type-2 to general type-2 – in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material. Presents fully updated material on new breakthroughs in human-inspired rule-based techniques for handling real-world uncertainties; Allows those already familiar with type-1 fuzzy se...

  14. Universal effect of dynamical reinforcement learning mechanism in spatial evolutionary games

    International Nuclear Information System (INIS)

    Zhang, Hai-Feng; Wu, Zhi-Xi; Wang, Bing-Hong

    2012-01-01

    One of the prototypical mechanisms in understanding the ubiquitous cooperation in social dilemma situations is the win–stay, lose–shift rule. In this work, a generalized win–stay, lose–shift learning model—a reinforcement learning model with dynamic aspiration level—is proposed to describe how humans adapt their social behaviors based on their social experiences. In the model, the players incorporate the information of the outcomes in previous rounds with time-dependent aspiration payoffs to regulate the probability of choosing cooperation. By investigating such a reinforcement learning rule in the spatial prisoner's dilemma game and public goods game, a most noteworthy viewpoint is that moderate greediness (i.e. moderate aspiration level) favors best the development and organization of collective cooperation. The generality of this observation is tested against different regulation strengths and different types of network of interaction as well. We also make comparisons with two recently proposed models to highlight the importance of the mechanism of adaptive aspiration level in supporting cooperation in structured populations

  15. Reinforcement learning: Solving two case studies

    Science.gov (United States)

    Duarte, Ana Filipa; Silva, Pedro; dos Santos, Cristina Peixoto

    2012-09-01

    Reinforcement Learning algorithms offer interesting features for the control of autonomous systems, such as the ability to learn from direct interaction with the environment, and the use of a simple reward signalas opposed to the input-outputs pairsused in classic supervised learning. The reward signal indicates the success of failure of the actions executed by the agent in the environment. In this work, are described RL algorithmsapplied to two case studies: the Crawler robot and the widely known inverted pendulum. We explore RL capabilities to autonomously learn a basic locomotion pattern in the Crawler, andapproach the balancing problem of biped locomotion using the inverted pendulum.

  16. Flow modelling of steel fibre reinforced self-compacting concrete

    DEFF Research Database (Denmark)

    Svec, Oldrich

    was done by means of the Immersed boundary method with direct forcing. Evolution of the immersed particles was described by Newton's differential equations of motion. The Newton's equations were solved by means of Runge-Kutta-Fehlberg iterative scheme. Several challenges had to be overcome during...... in concrete can efficiently substitute or supplement conventional steel reinforcement, such as reinforcement bars. Ordinary concrete composition further makes the material stiff and non-flowable. Self-compacting concrete is an alternative material of low yield stress and plastic viscosity that does flow...... of the fluid near formwork surface. A method to incorporate the apparent slip into the Lattice Boltzmann fluid dynamics solver was suggested. The proposed numerical framework was observed to correctly predict flow of fibre reinforced self-compacting concrete. The proposed numerical framework can therefore...

  17. Bond characteristics of steel fiber and deformed reinforcing steel bar embedded in steel fiber reinforced self-compacting concrete (SFRSCC)

    Science.gov (United States)

    Aslani, Farhad; Nejadi, Shami

    2012-09-01

    Steel fiber reinforced self-compacting concrete (SFRSCC) is a relatively new composite material which congregates the benefits of the self-compacting concrete (SCC) technology with the profits derived from the fiber addition to a brittle cementitious matrix. Steel fibers improve many of the properties of SCC elements including tensile strength, ductility, toughness, energy absorption capacity, fracture toughness and cracking. Although the available research regarding the influence of steel fibers on the properties of SFRSCC is limited, this paper investigates the bond characteristics between steel fiber and SCC firstly. Based on the available experimental results, the current analytical steel fiber pullout model (Dubey 1999) is modified by considering the different SCC properties and different fiber types (smooth, hooked) and inclination. In order to take into account the effect of fiber inclination in the pullout model, apparent shear strengths ( τ ( app)) and slip coefficient ( β) are incorporated to express the variation of pullout peak load and the augmentation of peak slip as the inclined angle increases. These variables are expressed as functions of the inclined angle ( ϕ). Furthurmore, steel-concrete composite floors, reinforced concrete floors supported by columns or walls and floors on an elastic foundations belong to the category of structural elements in which the conventional steel reinforcement can be partially replaced by the use of steel fibers. When discussing deformation capacity of structural elements or civil engineering structures manufactured using SFRSCC, one must be able to describe thoroughly both the behavior of the concrete matrix reinforced with steel fibers and the interaction between this composite matrix and discrete steel reinforcement of the conventional type. However, even though the knowledge on bond behavior is essential for evaluating the overall behavior of structural components containing reinforcement and steel fibers

  18. Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning

    Science.gov (United States)

    Talei, Amin; Chua, Lloyd Hock Chye; Quek, Chai; Jansson, Per-Erik

    2013-04-01

    SummaryA study using local learning Neuro-Fuzzy System (NFS) was undertaken for a rainfall-runoff modeling application. The local learning model was first tested on three different catchments: an outdoor experimental catchment measuring 25 m2 (Catchment 1), a small urban catchment 5.6 km2 in size (Catchment 2), and a large rural watershed with area of 241.3 km2 (Catchment 3). The results obtained from the local learning model were comparable or better than results obtained from physically-based, i.e. Kinematic Wave Model (KWM), Storm Water Management Model (SWMM), and Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The local learning algorithm also required a shorter training time compared to a global learning NFS model. The local learning model was next tested in real-time mode, where the model was continuously adapted when presented with current information in real time. The real-time implementation of the local learning model gave better results, without the need for retraining, when compared to a batch NFS model, where it was found that the batch model had to be retrained periodically in order to achieve similar results.

  19. Accelerating Multiagent Reinforcement Learning by Equilibrium Transfer.

    Science.gov (United States)

    Hu, Yujing; Gao, Yang; An, Bo

    2015-07-01

    An important approach in multiagent reinforcement learning (MARL) is equilibrium-based MARL, which adopts equilibrium solution concepts in game theory and requires agents to play equilibrium strategies at each state. However, most existing equilibrium-based MARL algorithms cannot scale due to a large number of computationally expensive equilibrium computations (e.g., computing Nash equilibria is PPAD-hard) during learning. For the first time, this paper finds that during the learning process of equilibrium-based MARL, the one-shot games corresponding to each state's successive visits often have the same or similar equilibria (for some states more than 90% of games corresponding to successive visits have similar equilibria). Inspired by this observation, this paper proposes to use equilibrium transfer to accelerate equilibrium-based MARL. The key idea of equilibrium transfer is to reuse previously computed equilibria when each agent has a small incentive to deviate. By introducing transfer loss and transfer condition, a novel framework called equilibrium transfer-based MARL is proposed. We prove that although equilibrium transfer brings transfer loss, equilibrium-based MARL algorithms can still converge to an equilibrium policy under certain assumptions. Experimental results in widely used benchmarks (e.g., grid world game, soccer game, and wall game) show that the proposed framework: 1) not only significantly accelerates equilibrium-based MARL (up to 96.7% reduction in learning time), but also achieves higher average rewards than algorithms without equilibrium transfer and 2) scales significantly better than algorithms without equilibrium transfer when the state/action space grows and the number of agents increases.

  20. Self-sensing CF-GFRP rods as mechanical reinforcement and sensors of concrete beams

    Science.gov (United States)

    Nanni, F.; Auricchio, F.; Sarchi, F.; Forte, G.; Gusmano, G.

    2006-02-01

    In this paper testing carried out on concrete beams reinforced with self-sensing composite rods is presented. Such concrete beams, whose peculiarity is to be reinforced by self-sensing materials able to generate an alarm signal when fixed loads are reached, were designed, manufactured and tested. The reinforcing rods were manufactured by pultrusion and consisted of self-sensing hybrid composites containing both glass and carbon fibres in an epoxy resin. The experimentation was carried out by performing simultaneously mechanical tests on the reinforced beams and electrical measurements on the composite rods. The results showed that the developed system reached the target proposed, giving an alarm signal.

  1. Efficient abstraction selection in reinforcement learning

    NARCIS (Netherlands)

    Seijen, H. van; Whiteson, S.; Kester, L.

    2013-01-01

    This paper introduces a novel approach for abstraction selection in reinforcement learning problems modelled as factored Markov decision processes (MDPs), for which a state is described via a set of state components. In abstraction selection, an agent must choose an abstraction from a set of

  2. Student Perceptions of and Confidence in Self-Care Course Concepts Using Team-based Learning.

    Science.gov (United States)

    Frame, Tracy R; Gryka, Rebecca; Kiersma, Mary E; Todt, Abby L; Cailor, Stephanie M; Chen, Aleda M H

    2016-04-25

    Objective. To evaluate changes in student perceptions of and confidence in self-care concepts after completing a team-based learning (TBL) self-care course. Methods. Team-based learning was used at two universities in first professional year, semester-long self-care courses. Two instruments were created and administered before and after the semester. The instruments were designed to assess changes in student perceptions of self-care using the theory of planned behavior (TPB) domains and confidence in learning self-care concepts using Bandura's Social Cognitive Theory. Wilcoxon signed rank tests were used to evaluate pre/post changes, and Mann Whitney U tests were used to evaluate university differences. Results. Fifty-three Cedarville University and 58 Manchester University students completed both instruments (100% and 92% response rates, respectively). Student self-care perceptions with TPB decreased significantly on nine of 13 items for Cedarville and decreased for one of 13 items for Manchester. Student confidence in self-care concepts improved significantly on all questions for both universities. Conclusion. Data indicate TBL self-care courses were effective in improving student confidence about self-care concepts. Establishing students' skill sets prior to entering the profession is beneficial because pharmacists will use self-directed learning to expand their knowledge and adapt to problem-solving situations.

  3. Self-Regulation and Problem Solving Ability in 7E-Learning Cycle Based Goal Orientation

    Science.gov (United States)

    Mulyono; Noor, N. L.

    2017-04-01

    Goal orientation differences between mastery goals and performance goals can be a cause of high and low self-regulation and problem-solving abilities. To overcome these problems applied 7E-learning cycle in which students learn and develop ways to optimise the power of reason through the learning phase elicit, engage, explore, explain, elaborate, evaluate, and extend. This study aimed to test the effectiveness of learning by 7E-learning cycle and describe self-regulation and mathematics problem solving based on goal-orientation after the implementation 7E-learning cycle. This study used mix method design with research subject is graders XII sciences MA NU Nurul Ulum Jekulo Kudus which divided into goal orientation is mastery goal and performance goal. The independent variable of this research is learning model, while the dependent variable is problem solving and self-regulation. Then, collecting data using scale, interviews and tests. The data processed with the proportion of test, t-test, paired samples t-test, and Normality-gain. The results show problem-solving abilities of students through 7E-learning cycle the average of mathematical problem-solving capability class, self-regulation at 7E-learning cycle is better than the traditional model study. The problem-solving skills at 7E-learning cycle are better than the traditional model study, there is an increase in self-regulation through 7E-learning cycle of 0.4 (medium), and there is an increased problem-solving ability through 7E-learning cycle by 0.79 (high). Based on the qualitative analysis, self-regulation and problem-solving ability after the implementation of 7E-learning cycle students of a mastery goal group are better than the performance goal team. It is suggested to implement 7E-learning cycle to improve self-regulation and problem-solving ability as well as directing and fostering mastery goal on the student in the learning process.

  4. Adaptive Functional-Based Neuro-Fuzzy-PID Incremental Controller Structure

    Directory of Open Access Journals (Sweden)

    Ashraf Ahmed Fahmy

    2014-03-01

    Full Text Available This paper presents an adaptive functional-based Neuro-fuzzy-PID incremental (NFPID controller structure that can be tuned either offline or online according to required controller performance. First, differential membership functions are used to represent the fuzzy membership functions of the input-output space of the three term controller. Second, controller rules are generated based on the discrete proportional, derivative, and integral function for the fuzzy space. Finally, a fully differentiable fuzzy neural network is constructed to represent the developed controller for either offline or online controller parameter adaptation.  Two different adaptation methods are used for controller tuning, offline method based on controller transient performance cost function optimization using Bees Algorithm, and online method based on tracking error minimization using back-propagation with momentum algorithm. The proposed control system was tested to show the validity of the controller structure over a fixed PID controller gains to control SCARA type robot arm.

  5. Increasing Student Metacognition and Learning through Classroom-Based Learning Communities and Self-Assessment

    Directory of Open Access Journals (Sweden)

    Amy Siegesmund

    2016-05-01

    Full Text Available Student overconfidence challenges success in introductory biology. This study examined the impact of classroom learning communities and self-assessment on student metacognition and subsequent impact on student epistemological beliefs, behaviors, and learning. Students wrote weekly self-assessments reflecting on the process of learning and received individual feedback. Students completed a learning strategies inventory focused on metacognition and study behaviors at the beginning and end of the semester and a Student Assessment of their Learning Gains (SALG at the end of the semester. Results indicated significant changes in both metacognition and study behaviors over the course of the semester, with a positive impact on learning as determined by broad and singular measures. Self-assessments and SALG data demonstrated a change in student beliefs and behaviors. Taken together, these findings argue that classroom learning communities and self-assessment can increase student metacognition and change student epistemological beliefs and behaviors.

  6. Design of a fuzzy logic based controller for neutron power regulation

    International Nuclear Information System (INIS)

    Velez D, D.

    2000-01-01

    This work presents a fuzzy logic controller design for neutron power control, from its source to its full power level, applied to a nuclear reactor model. First, we present the basic definitions on fuzzy sets as generalized definitions of the crisp (non fuzzy) set theory. Likewise, we define the basic operations on fuzzy sets (complement, union, and intersection), and the operations on fuzzy relations such as projection and cylindrical extension operations. Furthermore, some concepts of the fuzzy control theory, such as the main modules of the typical fuzzy controller structure and its internal variables, are defined. After the knowledge base is obtained by simulation of the reactor behavior, where the controlled system is modeled by a simple nonlinear reactor model, this model is used to infer a set of fuzzy rules for the reactor response to different insertions of reactivity. The reduction of the response time, using fuzzy rule based controllers on this reactor, is possible by adjusting the output membership functions, by selecting fuzzy rule sets, or by increasing the number of crisp inputs to the fuzzy controller. System characteristics, such as number of rules, response times, and safety parameter values, were considered in the evaluation of each controller merits. Different fuzzy controllers are designed to attain the desired power level, to maintain a constant level for long periods of time, and to keep the reactor away from a shutdown condition. The basic differences among the controllers are the number of crisp inputs and the novel implementation of a crisp power level-based selection of different sets of output membership functions. Simulation results highlight, mainly: (1) A decrease of the response variations at low power level, and (2) a decrease in the time required to attain the desired neutron power. Finally, we present a comparative study of different fuzzy control algorithms applied to a nuclear model. (Author)

  7. Ontology-based intelligent fuzzy agent for diabetes application

    NARCIS (Netherlands)

    Acampora, G.; Lee, C.-S.; Wang, M.-H.; Hsu, C.-Y.; Loia, V.

    2009-01-01

    It is widely pointed out that classical ontologies are not sufficient to deal with imprecise and vague knowledge for some real world applications, but the fuzzy ontology can effectively solve data and knowledge with uncertainty. In this paper, an ontology-based intelligent fuzzy agent (OIFA),

  8. 'Proactive' use of cue-context congruence for building reinforcement learning's reward function.

    Science.gov (United States)

    Zsuga, Judit; Biro, Klara; Tajti, Gabor; Szilasi, Magdolna Emma; Papp, Csaba; Juhasz, Bela; Gesztelyi, Rudolf

    2016-10-28

    Reinforcement learning is a fundamental form of learning that may be formalized using the Bellman equation. Accordingly an agent determines the state value as the sum of immediate reward and of the discounted value of future states. Thus the value of state is determined by agent related attributes (action set, policy, discount factor) and the agent's knowledge of the environment embodied by the reward function and hidden environmental factors given by the transition probability. The central objective of reinforcement learning is to solve these two functions outside the agent's control either using, or not using a model. In the present paper, using the proactive model of reinforcement learning we offer insight on how the brain creates simplified representations of the environment, and how these representations are organized to support the identification of relevant stimuli and action. Furthermore, we identify neurobiological correlates of our model by suggesting that the reward and policy functions, attributes of the Bellman equitation, are built by the orbitofrontal cortex (OFC) and the anterior cingulate cortex (ACC), respectively. Based on this we propose that the OFC assesses cue-context congruence to activate the most context frame. Furthermore given the bidirectional neuroanatomical link between the OFC and model-free structures, we suggest that model-based input is incorporated into the reward prediction error (RPE) signal, and conversely RPE signal may be used to update the reward-related information of context frames and the policy underlying action selection in the OFC and ACC, respectively. Furthermore clinical implications for cognitive behavioral interventions are discussed.

  9. The Influence of Guided Error-Based Learning on Motor Skills Self-Efficacy and Achievement.

    Science.gov (United States)

    Chien, Kuei-Pin; Chen, Sufen

    2018-01-01

    The authors investigated the role of errors in motor skills teaching, specifically the influence of errors on skills self-efficacy and achievement. The participants were 75 undergraduate students enrolled in pétanque courses. The experimental group (guided error-based learning, n = 37) received a 6-week period of instruction based on the students' errors, whereas the control group (correct motion instruction, n = 38) received a 6-week period of instruction emphasizing correct motor skills. The experimental group had significantly higher scores in motor skills self-efficacy and outcomes than did the control group. Novices' errors reflect their schema in motor skills learning, which provides a basis for instructors to implement student-centered instruction and to facilitate the learning process. Guided error-based learning can effectively enhance beginners' skills self-efficacy and achievement in precision sports such as pétanque.

  10. Effectiveness of simulation-based learning on student nurses' self-efficacy and performance while learning fundamental nursing skills.

    Science.gov (United States)

    Lin, Hsin-Hsin

    2015-01-01

    It was noted worldwide while learning fundamental skills and facing skills assessments, nursing students seemed to experience low confidence and high anxiety levels. Could simulation-based learning help to enhance students' self-efficacy and performance? Its effectiveness is mostly unidentified. This study was conducted to provide a shared experience to give nurse educators confidence and an insight into how simulation-based teaching can fit into nursing skills learning. A pilot study was completed with 50 second-year undergraduate nursing students, and the main study included 98 students where a pretest-posttest design was adopted. Data were gathered through four questionnaires and a performance assessment under scrutinized controls such as previous experiences, lecturers' teaching skills, duration of teaching, procedure of skills performance assessment and the inter-rater reliability. The results showed that simulation-based learning significantly improved students' self-efficacy regarding skills learning and the skills performance that nurse educators wish students to acquire. However, technology anxiety, examiners' critical attitudes towards students' performance and their unpredicted verbal and non-verbal expressions, have been found as possible confounding factors. The simulation-based learning proved to have a powerful positive effect on students' achievement outcomes. Nursing skills learning is one area that can benefit greatly from this kind of teaching and learning method.

  11. A Model to Explain the Emergence of Reward Expectancy neurons using Reinforcement Learning and Neural Network

    OpenAIRE

    Shinya, Ishii; Munetaka, Shidara; Katsunari, Shibata

    2006-01-01

    In an experiment of multi-trial task to obtain a reward, reward expectancy neurons,###which responded only in the non-reward trials that are necessary to advance###toward the reward, have been observed in the anterior cingulate cortex of monkeys.###In this paper, to explain the emergence of the reward expectancy neuron in###terms of reinforcement learning theory, a model that consists of a recurrent neural###network trained based on reinforcement learning is proposed. The analysis of the###hi...

  12. Reinforcement learning for dpm of embedded visual sensor nodes

    International Nuclear Information System (INIS)

    Khani, U.; Sadhayo, I. H.

    2014-01-01

    This paper proposes a RL (Reinforcement Learning) based DPM (Dynamic Power Management) technique to learn time out policies during a visual sensor node's operation which has multiple power/performance states. As opposed to the widely used static time out policies, our proposed DPM policy which is also referred to as OLTP (Online Learning of Time out Policies), learns to dynamically change the time out decisions in the different node states including the non-operational states. The selection of time out values in different power/performance states of a visual sensing platform is based on the workload estimates derived from a ML-ANN (Multi-Layer Artificial Neural Network) and an objective function given by weighted performance and power parameters. The DPM approach is also able to dynamically adjust the power-performance weights online to satisfy a given constraint of either power consumption or performance. Results show that the proposed learning algorithm explores the power-performance tradeoff with non-stationary workload and outperforms other DPM policies. It also performs the online adjustment of the tradeoff parameters in order to meet a user-specified constraint. (author)

  13. Adolescent-specific patterns of behavior and neural activity during social reinforcement learning.

    Science.gov (United States)

    Jones, Rebecca M; Somerville, Leah H; Li, Jian; Ruberry, Erika J; Powers, Alisa; Mehta, Natasha; Dyke, Jonathan; Casey, B J

    2014-06-01

    Humans are sophisticated social beings. Social cues from others are exceptionally salient, particularly during adolescence. Understanding how adolescents interpret and learn from variable social signals can provide insight into the observed shift in social sensitivity during this period. The present study tested 120 participants between the ages of 8 and 25 years on a social reinforcement learning task where the probability of receiving positive social feedback was parametrically manipulated. Seventy-eight of these participants completed the task during fMRI scanning. Modeling trial-by-trial learning, children and adults showed higher positive learning rates than did adolescents, suggesting that adolescents demonstrated less differentiation in their reaction times for peers who provided more positive feedback. Forming expectations about receiving positive social reinforcement correlated with neural activity within the medial prefrontal cortex and ventral striatum across age. Adolescents, unlike children and adults, showed greater insular activity during positive prediction error learning and increased activity in the supplementary motor cortex and the putamen when receiving positive social feedback regardless of the expected outcome, suggesting that peer approval may motivate adolescents toward action. While different amounts of positive social reinforcement enhanced learning in children and adults, all positive social reinforcement equally motivated adolescents. Together, these findings indicate that sensitivity to peer approval during adolescence goes beyond simple reinforcement theory accounts and suggest possible explanations for how peers may motivate adolescent behavior.

  14. Social learning through prediction error in the brain

    Science.gov (United States)

    Joiner, Jessica; Piva, Matthew; Turrin, Courtney; Chang, Steve W. C.

    2017-06-01

    Learning about the world is critical to survival and success. In social animals, learning about others is a necessary component of navigating the social world, ultimately contributing to increasing evolutionary fitness. How humans and nonhuman animals represent the internal states and experiences of others has long been a subject of intense interest in the developmental psychology tradition, and, more recently, in studies of learning and decision making involving self and other. In this review, we explore how psychology conceptualizes the process of representing others, and how neuroscience has uncovered correlates of reinforcement learning signals to explore the neural mechanisms underlying social learning from the perspective of representing reward-related information about self and other. In particular, we discuss self-referenced and other-referenced types of reward prediction errors across multiple brain structures that effectively allow reinforcement learning algorithms to mediate social learning. Prediction-based computational principles in the brain may be strikingly conserved between self-referenced and other-referenced information.

  15. Self-organized Learning Environments

    DEFF Research Database (Denmark)

    Dalsgaard, Christian; Mathiasen, Helle

    2007-01-01

    system actively. The two groups used the system in their own way to support their specific activities and ways of working. The paper concludes that self-organized learning environments can strengthen the development of students’ academic as well as social qualifications. Further, the paper identifies......The purpose of the paper is to discuss the potentials of using a conference system in support of a project based university course. We use the concept of a self-organized learning environment to describe the shape of the course. In the paper we argue that educational technology, such as conference...... systems, has a potential to support students’ development of self-organized learning environments and facilitate self-governed activities in higher education. The paper is based on an empirical study of two project groups’ use of a conference system. The study showed that the students used the conference...

  16. Time representation in reinforcement learning models of the basal ganglia

    Directory of Open Access Journals (Sweden)

    Samuel Joseph Gershman

    2014-01-01

    Full Text Available Reinforcement learning models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection. Time plays an important role in these models, but there is still no theoretical consensus about what kind of time representation is used by the basal ganglia. We review several theoretical accounts and their supporting evidence. We then discuss the relationship between reinforcement learning models and the timing mechanisms that have been attributed to the basal ganglia. We hypothesize that a single computational system may underlie both reinforcement learning and interval timing—the perception of duration in the range of seconds to hours. This hypothesis, which extends earlier models by incorporating a time-sensitive action selection mechanism, may have important implications for understanding disorders like Parkinson's disease in which both decision making and timing are impaired.

  17. A Fuzzy Rule-based Controller For Automotive Vehicle Guidance

    OpenAIRE

    Hessburg, Thomas; Tomizuka, Masayoshi

    1991-01-01

    A fuzzy rule-based controller is applied to lateral guidance of a vehicle for an automated highway system. The fuzzy rules, based on human drivers' experiences, are developed to track the center of a lane in the presence of external disturbances and over a range of vehicle operating conditions.

  18. Models for cooperative games with fuzzy relations among the agents fuzzy communication, proximity relation and fuzzy permission

    CERN Document Server

    Jiménez-Losada, Andrés

    2017-01-01

    This book offers a comprehensive introduction to cooperative game theory and a practice-oriented reference guide to new models and tools for studying bilateral fuzzy relations among several agents or players. It introduces the reader to several fuzzy models, each of which is first analyzed in the context of classical games (crisp games) and subsequently in the context of fuzzy games. Special emphasis is given to the value of Shapley, which is presented for the first time in the context of fuzzy games. Students and researchers will find here a self-contained reference guide to cooperative fuzzy games, characterized by a wealth of examples, descriptions of a wide range of possible situations, step-by-step explanations of the basic mathematical concepts involved, and easy-to-follow information on axioms and properties.

  19. A fuzzy logic based PROMETHEE method for material selection problems

    Directory of Open Access Journals (Sweden)

    Muhammet Gul

    2018-03-01

    Full Text Available Material selection is a complex problem in the design and development of products for diverse engineering applications. This paper presents a fuzzy PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation method based on trapezoidal fuzzy interval numbers that can be applied to the selection of materials for an automotive instrument panel. Also, it presents uniqueness in making a significant contribution to the literature in terms of the application of fuzzy decision-making approach to material selection problems. The method is illustrated, validated, and compared against three different fuzzy MCDM methods (fuzzy VIKOR, fuzzy TOPSIS, and fuzzy ELECTRE in terms of its ranking performance. Also, the relationships between the compared methods and the proposed scenarios for fuzzy PROMETHEE are evaluated via the Spearman’s correlation coefficient. Styrene Maleic Anhydride and Polypropylene are determined optionally as suitable materials for the automotive instrument panel case. We propose a generic fuzzy MCDM methodology that can be practically implemented to material selection problem. The main advantages of the methodology are consideration of the vagueness, uncertainty, and fuzziness to decision making environment.

  20. An effect of immediate reinforcement and delayed punishment, with possible implications for self-control.

    Science.gov (United States)

    Epstein, R

    1984-12-01

    Behavior said to show self-control occurs virtually always as an alternative to behavior that produces conflicting consequences. One class of such consequences, immediate reinforcement and delayed punishment, is especially pervasive. Three experiments are described in which an effect of immediate reinforcement and delayed punishment is demonstrated. The results suggest that when immediate reinforcement and delayed punishment are imminent, the reinforcer alone controls the organism's behavior (in other words the organism behaves "impulsively"). The key to self-control, therefore, may be the acquisition of a large number of avoidance behaviors relevant to reinforcers that are correlated with delayed punishment. Human self-control may indeed involve such a process but undoubtedly involves others as well.

  1. Safe Exploration of State and Action Spaces in Reinforcement Learning

    OpenAIRE

    Garcia, Javier; Fernandez, Fernando

    2014-01-01

    In this paper, we consider the important problem of safe exploration in reinforcement learning. While reinforcement learning is well-suited to domains with complex transition dynamics and high-dimensional state-action spaces, an additional challenge is posed by the need for safe and efficient exploration. Traditional exploration techniques are not particularly useful for solving dangerous tasks, where the trial and error process may lead to the selection of actions whose execution in some sta...

  2. An adaptive map-matching algorithm based on hierarchical fuzzy system from vehicular GPS data.

    Directory of Open Access Journals (Sweden)

    Jinjun Tang

    Full Text Available An improved hierarchical fuzzy inference method based on C-measure map-matching algorithm is proposed in this paper, in which the C-measure represents the certainty or probability of the vehicle traveling on the actual road. A strategy is firstly introduced to use historical positioning information to employ curve-curve matching between vehicle trajectories and shapes of candidate roads. It improves matching performance by overcoming the disadvantage of traditional map-matching algorithm only considering current information. An average historical distance is used to measure similarity between vehicle trajectories and road shape. The input of system includes three variables: distance between position point and candidate roads, angle between driving heading and road direction, and average distance. As the number of fuzzy rules will increase exponentially when adding average distance as a variable, a hierarchical fuzzy inference system is then applied to reduce fuzzy rules and improve the calculation efficiency. Additionally, a learning process is updated to support the algorithm. Finally, a case study contains four different routes in Beijing city is used to validate the effectiveness and superiority of the proposed method.

  3. Fuzzy Logic Based Autonomous Traffic Control System

    Directory of Open Access Journals (Sweden)

    Muhammad ABBAS

    2012-01-01

    Full Text Available The aim of this paper is to design and implement fuzzy logic based traffic light Control system to solve the traffic congestion issues. In this system four input parameters: Arrival, Queue, Pedestrian and Emergency Vehicle and two output parameters: Extension in Green and Pedestrian Signals are used. Using Fuzzy Rule Base, the system extends or terminates the Green Signal according to the Traffic situation at the junction. On the presence of emergency vehicle, the system decides which signal(s should be red and how much an extension should be given to Green Signal for Emergency Vehicle. The system also monitors the density of people and makes decisions accordingly. In order to verify the proposed design algorithm MATLAB simulation is adopted and results obtained show concurrency to the calculated values according to the Mamdani Model of the Fuzzy Control System.

  4. Adversarial Reinforcement Learning in a Cyber Security Simulation}

    OpenAIRE

    Elderman, Richard; Pater, Leon; Thie, Albert; Drugan, Madalina; Wiering, Marco

    2017-01-01

    This paper focuses on cyber-security simulations in networks modeled as a Markov game with incomplete information and stochastic elements. The resulting game is an adversarial sequential decision making problem played with two agents, the attacker and defender. The two agents pit one reinforcement learning technique, like neural networks, Monte Carlo learning and Q-learning, against each other and examine their effectiveness against learning opponents. The results showed that Monte Carlo lear...

  5. Feasibility of externally activated self-repairing concrete with epoxy injection network and Cu-Al-Mn superelastic alloy reinforcing bars

    International Nuclear Information System (INIS)

    Pareek, Sanjay; Shrestha, Kshitij C; Araki, Yoshikazu; Suzuki, Yusuke; Omori, Toshihiro; Kainuma, Ryosuke

    2014-01-01

    This paper studies the effectiveness of an externally activated self-repairing technique for concrete members with epoxy injection network and Cu-Al-Mn superelastic alloy (SEA) reinforcing bars (rebars). Compared to existing crack self-repairing and self-healing techniques, the epoxy injection network has the following strengths: (1) Different from the self-repairing methods using brittle containers or tubes for adhesives, the proposed self-repair process can be performed repeatedly and is feasible for onsite concrete casting. (2) Different from the autogenic self-healing techniques, full strength recovery can be achieved in a shorter time period without the necessity of water. This paper attempts to enhance the self-repairing capability of the epoxy injection network by reducing residual cracks by using cost-effective Cu-based SEA bars. The effectiveness of the present technique is examined using concrete beam specimens reinforced by 3 types of bars. The first specimen is reinforced by steel deformed bars, the second by steel threaded bars, and finally by SEA threaded rebars. The tests were performed with a 3 point cyclic loading with increasing amplitude. From the test results, effective self-repairing was confirmed for small deformation levels irrespective of the reinforcement types. Effective self-repairing was observed in the SEA reinforced specimen even under much larger deformations. Nonlinear finite element analysis was performed to confirm the experimental findings. (paper)

  6. Effects of the Badge Mechanism on Self-Efficacy and Learning Performance in a Game-Based English Learning Environment

    Science.gov (United States)

    Yang, Jie Chi; Quadir, Benazir; Chen, Nian-Shing

    2016-01-01

    A growing number of studies have been conducted on digital game-based learning (DGBL). However, there has been a lack of attention paid to individuals' self-efficacy and learning performance in the implementation of DGBL. This study therefore investigated how the badge mechanism in DGBL enhanced users' self-efficacy in the subject domain of…

  7. Logical Characterisation of Ontology Construction using Fuzzy Description Logics

    DEFF Research Database (Denmark)

    Badie, Farshad; Götzsche, Hans

    had the extension of ontologies with Fuzzy Logic capabilities which plan to make proper backgrounds for ontology driven reasoning and argumentation on vague and imprecise domains. This presentation conceptualises learning from fuzzy classes using the Inductive Logic Programming framework. Then......, employs Description Logics in characterising and analysing fuzzy statements. And finally, provides a conceptual framework describing fuzzy concept learning in ontologies using the Inductive Logic Programming....

  8. Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing

    NARCIS (Netherlands)

    Le, M.N.; Fokkens, A.S.

    Error propagation is a common problem in NLP. Reinforcement learning explores erroneous states during training and can therefore be more robust when mistakes are made early in a process. In this paper, we apply reinforcement learning to greedy dependency parsing which is known to suffer from error

  9. The Computational Development of Reinforcement Learning during Adolescence.

    Directory of Open Access Journals (Sweden)

    Stefano Palminteri

    2016-06-01

    Full Text Available Adolescence is a period of life characterised by changes in learning and decision-making. Learning and decision-making do not rely on a unitary system, but instead require the coordination of different cognitive processes that can be mathematically formalised as dissociable computational modules. Here, we aimed to trace the developmental time-course of the computational modules responsible for learning from reward or punishment, and learning from counterfactual feedback. Adolescents and adults carried out a novel reinforcement learning paradigm in which participants learned the association between cues and probabilistic outcomes, where the outcomes differed in valence (reward versus punishment and feedback was either partial or complete (either the outcome of the chosen option only, or the outcomes of both the chosen and unchosen option, were displayed. Computational strategies changed during development: whereas adolescents' behaviour was better explained by a basic reinforcement learning algorithm, adults' behaviour integrated increasingly complex computational features, namely a counterfactual learning module (enabling enhanced performance in the presence of complete feedback and a value contextualisation module (enabling symmetrical reward and punishment learning. Unlike adults, adolescent performance did not benefit from counterfactual (complete feedback. In addition, while adults learned symmetrically from both reward and punishment, adolescents learned from reward but were less likely to learn from punishment. This tendency to rely on rewards and not to consider alternative consequences of actions might contribute to our understanding of decision-making in adolescence.

  10. A DIFFERENT WEB-BASED GEOCODING SERVICE USING FUZZY TECHNIQUES

    Directory of Open Access Journals (Sweden)

    P. Pahlavani

    2015-12-01

    Full Text Available Geocoding – the process of finding position based on descriptive data such as address or postal code - is considered as one of the most commonly used spatial analyses. Many online map providers such as Google Maps, Bing Maps and Yahoo Maps present geocoding as one of their basic capabilities. Despite the diversity of geocoding services, users usually face some limitations when they use available online geocoding services. In existing geocoding services, proximity and nearness concept is not modelled appropriately as well as these services search address only by address matching based on descriptive data. In addition there are also some limitations in display searching results. Resolving these limitations can enhance efficiency of the existing geocoding services. This paper proposes the idea of integrating fuzzy technique with geocoding process to resolve these limitations. In order to implement the proposed method, a web-based system is designed. In proposed method, nearness to places is defined by fuzzy membership functions and multiple fuzzy distance maps are created. Then these fuzzy distance maps are integrated using fuzzy overlay technique for obtain the results. Proposed methods provides different capabilities for users such as ability to search multi-part addresses, searching places based on their location, non-point representation of results as well as displaying search results based on their priority.

  11. Implementation of Fuzzy Logic Based Temperature-Controlled Heat ...

    African Journals Online (AJOL)

    This research then compares the control performance of PID (Proportional Integral and Derivative) and Fuzzy logic controllers. Conclusions are made based on these control performances. The results show that the control performance for a Fuzzy controller is quite similar to PID controller but comparatively gives a better ...

  12. Polynomial fuzzy model-based approach for underactuated surface vessels

    DEFF Research Database (Denmark)

    Khooban, Mohammad Hassan; Vafamand, Navid; Dragicevic, Tomislav

    2018-01-01

    The main goal of this study is to introduce a new polynomial fuzzy model-based structure for a class of marine systems with non-linear and polynomial dynamics. The suggested technique relies on a polynomial Takagi–Sugeno (T–S) fuzzy modelling, a polynomial dynamic parallel distributed compensation...... surface vessel (USV). Additionally, in order to overcome the USV control challenges, including the USV un-modelled dynamics, complex nonlinear dynamics, external disturbances and parameter uncertainties, the polynomial fuzzy model representation is adopted. Moreover, the USV-based control structure...... and a sum-of-squares (SOS) decomposition. The new proposed approach is a generalisation of the standard T–S fuzzy models and linear matrix inequality which indicated its effectiveness in decreasing the tracking time and increasing the efficiency of the robust tracking control problem for an underactuated...

  13. HYBRID SYSTEM BASED FUZZY-PID CONTROL SCHEMES FOR UNPREDICTABLE PROCESS

    Directory of Open Access Journals (Sweden)

    M.K. Tan

    2011-07-01

    Full Text Available In general, the primary aim of polymerization industry is to enhance the process operation in order to obtain high quality and purity product. However, a sudden and large amount of heat will be released rapidly during the mixing process of two reactants, i.e. phenol and formalin due to its exothermic behavior. The unpredictable heat will cause deviation of process temperature and hence affect the quality of the product. Therefore, it is vital to control the process temperature during the polymerization. In the modern industry, fuzzy logic is commonly used to auto-tune PID controller to control the process temperature. However, this method needs an experienced operator to fine tune the fuzzy membership function and universe of discourse via trial and error approach. Hence, the setting of fuzzy inference system might not be accurate due to the human errors. Besides that, control of the process can be challenging due to the rapid changes in the plant parameters which will increase the process complexity. This paper proposes an optimization scheme using hybrid of Q-learning (QL and genetic algorithm (GA to optimize the fuzzy membership function in order to allow the conventional fuzzy-PID controller to control the process temperature more effectively. The performances of the proposed optimization scheme are compared with the existing fuzzy-PID scheme. The results show that the proposed optimization scheme is able to control the process temperature more effectively even if disturbance is introduced.

  14. Reinforcement learning agents providing advice in complex video games

    Science.gov (United States)

    Taylor, Matthew E.; Carboni, Nicholas; Fachantidis, Anestis; Vlahavas, Ioannis; Torrey, Lisa

    2014-01-01

    This article introduces a teacher-student framework for reinforcement learning, synthesising and extending material that appeared in conference proceedings [Torrey, L., & Taylor, M. E. (2013)]. Teaching on a budget: Agents advising agents in reinforcement learning. {Proceedings of the international conference on autonomous agents and multiagent systems}] and in a non-archival workshop paper [Carboni, N., &Taylor, M. E. (2013, May)]. Preliminary results for 1 vs. 1 tactics in StarCraft. {Proceedings of the adaptive and learning agents workshop (at AAMAS-13)}]. In this framework, a teacher agent instructs a student agent by suggesting actions the student should take as it learns. However, the teacher may only give such advice a limited number of times. We present several novel algorithms that teachers can use to budget their advice effectively, and we evaluate them in two complex video games: StarCraft and Pac-Man. Our results show that the same amount of advice, given at different moments, can have different effects on student learning, and that teachers can significantly affect student learning even when students use different learning methods and state representations.

  15. Comparing problem-based learning students to students in a lecture-based curriculum: learning strategies and the relation with self-study time

    OpenAIRE

    Wijnen, Marit; Loyens, Sofie; Smeets, Guus; Kroeze, Maarten; Molen, Henk

    2017-01-01

    textabstractIn educational theory, deep processing (i.e., connecting different study topics together) and self-regulation (i.e., taking control over one’s own learning process) are considered effective learning strategies. These learning strategies can be influenced by the learning environment. Problem-based learning (PBL), a student-centered educational method, is believed to stimulate the use of these effective learning strategies. Several aspects of PBL such as discussions of real-life pro...

  16. Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques.

    Science.gov (United States)

    Chen, Shyi-Ming; Manalu, Gandhi Maruli Tua; Pan, Jeng-Shyang; Liu, Hsiang-Chuan

    2013-06-01

    In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization (PSO) techniques. First, we fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors second-order fuzzy logical relationships. Then, we group the two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, we obtain the optimal weighting vector for each fuzzy-trend logical relationship group by using PSO techniques to perform the forecasting. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index and the NTD/USD exchange rates. The experimental results show that the proposed method gets better forecasting performance than the existing methods.

  17. Locomotion training of legged robots using hybrid machine learning techniques

    Science.gov (United States)

    Simon, William E.; Doerschuk, Peggy I.; Zhang, Wen-Ran; Li, Andrew L.

    1995-01-01

    In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible

  18. Rules, Models, and Self-Reinforcement in Children

    Science.gov (United States)

    Hildebrandt, David E.; And Others

    1973-01-01

    The study found that concordance between a rule and a recent model's behavior was most effective in leading to acceptance of the rule, despite a tendency for the subject's to adopt lenient self-reinforcement criteria when given an opportunity to do so. (JB)

  19. Data-Based Control for Humanoid Robots Using Support Vector Regression, Fuzzy Logic, and Cubature Kalman Filter

    Directory of Open Access Journals (Sweden)

    Liyang Wang

    2016-01-01

    Full Text Available Time-varying external disturbances cause instability of humanoid robots or even tip robots over. In this work, a trapezoidal fuzzy least squares support vector regression- (TF-LSSVR- based control system is proposed to learn the external disturbances and increase the zero-moment-point (ZMP stability margin of humanoid robots. First, the humanoid states and the corresponding control torques of the joints for training the controller are collected by implementing simulation experiments. Secondly, a TF-LSSVR with a time-related trapezoidal fuzzy membership function (TFMF is proposed to train the controller using the simulated data. Thirdly, the parameters of the proposed TF-LSSVR are updated using a cubature Kalman filter (CKF. Simulation results are provided. The proposed method is shown to be effective in learning and adapting occasional external disturbances and ensuring the stability margin of the robot.

  20. Type-2 fuzzy logic uncertain systems’ modeling and control

    CERN Document Server

    Antão, Rómulo

    2017-01-01

    This book focuses on a particular domain of Type-2 Fuzzy Logic, related to process modeling and control applications. It deepens readers’understanding of Type-2 Fuzzy Logic with regard to the following three topics: using simpler methods to train a Type-2 Takagi-Sugeno Fuzzy Model; using the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on a locally linear n-step ahead predictor; and developing model-based control algorithms according to the Generalized Predictive Control principles using Type-2 Fuzzy Sets. Throughout the book, theory is always complemented with practical applications and readers are invited to take their learning process one step farther and implement their own applications using the algorithms’ source codes (provided). As such, the book offers avaluable referenceguide for allengineers and researchers in the field ofcomputer science who are interested in intelligent systems, rule-based systems and modeling uncertainty.

  1. FUZZY BASED CONTRAST STRETCHING FOR MEDICAL IMAGE ENHANCEMENT

    Directory of Open Access Journals (Sweden)

    T.C. Raja Kumar

    2011-07-01

    Full Text Available Contrast Stretching is an important part in medical image processing applications. Contrast is the difference between two adjacent pixels. Fuzzy statistical values are analyzed and better results are produced in the spatial domain of the input image. The histogram mapping produces the resultant image with less impulsive noise and smooth nature. The probabilities of gray values are generated and the fuzzy set is determined from the position of the input image pixel. The result indicates the good performance of the proposed fuzzy based stretching. The inverse transform of the real values are mapped with the input image to generate the fuzzy statistics. This approach gives a flexible image enhancement for medical images in the presence of noises.

  2. Applications of Deep Learning and Reinforcement Learning to Biological Data.

    Science.gov (United States)

    Mahmud, Mufti; Kaiser, Mohammed Shamim; Hussain, Amir; Vassanelli, Stefano

    2018-06-01

    Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

  3. Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems

    Science.gov (United States)

    El-Sebakhy, Emad A.

    2009-09-01

    Pressure-volume-temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson-Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.

  4. Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization

    International Nuclear Information System (INIS)

    Yu, Kunjie; Chen, Xu; Wang, Xin; Wang, Zhenlei

    2017-01-01

    Highlights: • SATLBO is proposed to identify the PV model parameters efficiently. • In SATLBO, the learners self-adaptively select different learning phases. • An elite learning is developed in teacher phase to perform local searching. • A diversity learning is proposed in learner phase to maintain population diversity. • SATLBO achieves the first in ranking on overall performance among nine algorithms. - Abstract: Parameters identification of photovoltaic (PV) model based on measured current-voltage characteristic curves plays an important role in the simulation and evaluation of PV systems. To accurately and reliably identify the PV model parameters, a self-adaptive teaching-learning-based optimization (SATLBO) is proposed in this paper. In SATLBO, the learners can self-adaptively select different learning phases based on their knowledge level. The better learners are more likely to choose the learner phase for improving the population diversity, while the worse learners tend to choose the teacher phase to enhance the convergence rate. Thus, learners at different levels focus on different searching abilities to efficiently enhance the performance of algorithm. In addition, to improve the searching ability of different learning phases, an elite learning strategy and a diversity learning method are introduced into the teacher phase and learner phase, respectively. The performance of SATLBO is firstly evaluated on 34 benchmark functions, and experimental results show that SATLBO achieves the first in ranking on the overall performance among nine algorithms. Then, SATLBO is employed to identify parameters of different PV models, i.e., single diode, double diode, and PV module. Experimental results indicate that SATLBO exhibits high accuracy and reliability compared with other parameter extraction methods.

  5. Emotion in reinforcement learning agents and robots: A survey

    OpenAIRE

    Moerland, T.M.; Broekens, D.J.; Jonker, C.M.

    2018-01-01

    This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent's decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for ...

  6. Face Recognition Method Based on Fuzzy 2DPCA

    Directory of Open Access Journals (Sweden)

    Xiaodong Li

    2014-01-01

    Full Text Available 2DPCA, which is one of the most important face recognition methods, is relatively sensitive to substantial variations in light direction, face pose, and facial expression. In order to improve the recognition performance of the traditional 2DPCA, a new 2DPCA algorithm based on the fuzzy theory is proposed in this paper, namely, the fuzzy 2DPCA (F2DPCA. In this method, applying fuzzy K-nearest neighbor (FKNN, the membership degree matrix of the training samples is calculated, which is used to get the fuzzy means of each class. The average of fuzzy means is then incorporated into the definition of the general scatter matrix with anticipation that it can improve classification result. The comprehensive experiments on the ORL, the YALE, and the FERET face database show that the proposed method can improve the classification rates and reduce the sensitivity to variations between face images caused by changes in illumination, face expression, and face pose.

  7. Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

    Directory of Open Access Journals (Sweden)

    DimitrisG. Stavrakoudis

    2012-04-01

    Full Text Available This paper introduces the Fast Iterative Rule-based Linguistic Classifier (FaIRLiC, a Genetic Fuzzy Rule-Based Classification System (GFRBCS which targets at reducing the structural complexity of the resulting rule base, as well as its learning algorithm's computational requirements, especially when dealing with high-dimensional feature spaces. The proposed methodology follows the principles of the iterative rule learning (IRL approach, whereby a rule extraction algorithm (REA is invoked in an iterative fashion, producing one fuzzy rule at a time. The REA is performed in two successive steps: the first one selects the relevant features of the currently extracted rule, whereas the second one decides the antecedent part of the fuzzy rule, using the previously selected subset of features. The performance of the classifier is finally optimized through a genetic tuning post-processing stage. Comparative results in a hyperspectral remote sensing classification as well as in 12 real-world classification datasets indicate the effectiveness of the proposed methodology in generating high-performing and compact fuzzy rule-based classifiers, even for very high-dimensional feature spaces.

  8. Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain.

    Science.gov (United States)

    Niv, Yael; Edlund, Jeffrey A; Dayan, Peter; O'Doherty, John P

    2012-01-11

    Humans and animals are exquisitely, though idiosyncratically, sensitive to risk or variance in the outcomes of their actions. Economic, psychological, and neural aspects of this are well studied when information about risk is provided explicitly. However, we must normally learn about outcomes from experience, through trial and error. Traditional models of such reinforcement learning focus on learning about the mean reward value of cues and ignore higher order moments such as variance. We used fMRI to test whether the neural correlates of human reinforcement learning are sensitive to experienced risk. Our analysis focused on anatomically delineated regions of a priori interest in the nucleus accumbens, where blood oxygenation level-dependent (BOLD) signals have been suggested as correlating with quantities derived from reinforcement learning. We first provide unbiased evidence that the raw BOLD signal in these regions corresponds closely to a reward prediction error. We then derive from this signal the learned values of cues that predict rewards of equal mean but different variance and show that these values are indeed modulated by experienced risk. Moreover, a close neurometric-psychometric coupling exists between the fluctuations of the experience-based evaluations of risky options that we measured neurally and the fluctuations in behavioral risk aversion. This suggests that risk sensitivity is integral to human learning, illuminating economic models of choice, neuroscientific models of affective learning, and the workings of the underlying neural mechanisms.

  9. Reinforcement Learning Explains Conditional Cooperation and Its Moody Cousin.

    Directory of Open Access Journals (Sweden)

    Takahiro Ezaki

    2016-07-01

    Full Text Available Direct reciprocity, or repeated interaction, is a main mechanism to sustain cooperation under social dilemmas involving two individuals. For larger groups and networks, which are probably more relevant to understanding and engineering our society, experiments employing repeated multiplayer social dilemma games have suggested that humans often show conditional cooperation behavior and its moody variant. Mechanisms underlying these behaviors largely remain unclear. Here we provide a proximate account for this behavior by showing that individuals adopting a type of reinforcement learning, called aspiration learning, phenomenologically behave as conditional cooperator. By definition, individuals are satisfied if and only if the obtained payoff is larger than a fixed aspiration level. They reinforce actions that have resulted in satisfactory outcomes and anti-reinforce those yielding unsatisfactory outcomes. The results obtained in the present study are general in that they explain extant experimental results obtained for both so-called moody and non-moody conditional cooperation, prisoner's dilemma and public goods games, and well-mixed groups and networks. Different from the previous theory, individuals are assumed to have no access to information about what other individuals are doing such that they cannot explicitly use conditional cooperation rules. In this sense, myopic aspiration learning in which the unconditional propensity of cooperation is modulated in every discrete time step explains conditional behavior of humans. Aspiration learners showing (moody conditional cooperation obeyed a noisy GRIM-like strategy. This is different from the Pavlov, a reinforcement learning strategy promoting mutual cooperation in two-player situations.

  10. A strategy learning model for autonomous agents based on classification

    Directory of Open Access Journals (Sweden)

    Śnieżyński Bartłomiej

    2015-09-01

    Full Text Available In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

  11. Fabrication and mechanical properties of self-reinforced poly(ethylene terephthalate composites

    Directory of Open Access Journals (Sweden)

    2011-03-01

    Full Text Available Self-reinforced poly(ethylene terephthalate (PET composites prepared by using a modified film-stacking technique were examined in this study. The starting materials included a high tenacity PET yarn (reinforcement and a low melting temperature biodegradable polyester resin (matrix, both of which differ in their melting temperatures with a value of 56°C. This experiment produced composite sheets at three consolidation temperatures (Tc: 215, 225, and 235°C at a constant holding time (th: 6.5 min, and three holding times (3, 6.5 and 10 min at a constant consolidation temperature of 225°C. This study observed a significant improvement in the mechanical properties obtained in self-reinforced PET composites compared to the pure polyester resin. The results of tensile, flexural, and Izod impact tests proved that optimal conditions are low consolidation temperature and short holding time. The absorbed impact energy of the best self-reinforced PET composite material was 854.0 J/m, which is 63 times that of pure polyester resin.

  12. On Intuitionistic Fuzzy Filters of Intuitionistic Fuzzy Coframes

    Directory of Open Access Journals (Sweden)

    Rajesh K. Thumbakara

    2013-01-01

    Full Text Available Frame theory is the study of topology based on its open set lattice, and it was studied extensively by various authors. In this paper, we study quotients of intuitionistic fuzzy filters of an intuitionistic fuzzy coframe. The quotients of intuitionistic fuzzy filters are shown to be filters of the given intuitionistic fuzzy coframe. It is shown that the collection of all intuitionistic fuzzy filters of a coframe and the collection of all intutionistic fuzzy quotient filters of an intuitionistic fuzzy filter are coframes.

  13. Scheduled power tracking control of the wind-storage hybrid system based on the reinforcement learning theory

    Science.gov (United States)

    Li, Ze

    2017-09-01

    In allusion to the intermittency and uncertainty of the wind electricity, energy storage and wind generator are combined into a hybrid system to improve the controllability of the output power. A scheduled power tracking control method is proposed based on the reinforcement learning theory and Q-learning algorithm. In this method, the state space of the environment is formed with two key factors, i.e. the state of charge of the energy storage and the difference value between the actual wind power and scheduled power, the feasible action is the output power of the energy storage, and the corresponding immediate rewarding function is designed to reflect the rationality of the control action. By interacting with the environment and learning from the immediate reward, the optimal control strategy is gradually formed. After that, it could be applied to the scheduled power tracking control of the hybrid system. Finally, the rationality and validity of the method are verified through simulation examples.

  14. Reinforcement Learning for a New Piano Mover

    Directory of Open Access Journals (Sweden)

    Yuko Ishiwaka

    2005-08-01

    Full Text Available We attempt to achieve corporative behavior of autonomous decentralized agents constructed via Q-Learning, which is a type of reinforcement learning. As such, in the present paper, we examine the piano mover's problem. We propose a multi-agent architecture that has a training agent, learning agents and intermediate agent. Learning agents are heterogeneous and can communicate with each other. The movement of an object with three kinds of agent depends on the composition of the actions of the learning agents. By learning its own shape through the learning agents, avoidance of obstacles by the object is expected. We simulate the proposed method in a two-dimensional continuous world. Results obtained in the present investigation reveal the effectiveness of the proposed method.

  15. Fuzzy Genetic Algorithm Based on Principal Operation and Inequity Degree

    Science.gov (United States)

    Li, Fachao; Jin, Chenxia

    In this paper, starting from the structure of fuzzy information, by distinguishing principal indexes and assistant indexes, give comparison of fuzzy information on synthesizing effect and operation of fuzzy optimization on principal indexes transformation, further, propose axiom system of fuzzy inequity degree from essence of constraint, and give an instructive metric method; Then, combining genetic algorithm, give fuzzy optimization methods based on principal operation and inequity degree (denoted by BPO&ID-FGA, for short); Finally, consider its convergence using Markov chain theory and analyze its performance through an example. All these indicate, BPO&ID-FGA can not only effectively merge decision consciousness into the optimization process, but possess better global convergence, so it can be applied to many fuzzy optimization problems.

  16. Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

    Directory of Open Access Journals (Sweden)

    Erdal Kayacan

    2017-01-01

    Full Text Available A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack of modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural network (FNN is used in parallel with a conventional P (proportional controller. Among the learning algorithms in the literature, a derivative-free one, sliding mode control (SMC theory-based learning algorithm, is preferred as it has been proved to be computationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning algorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned aerial vehicles (UAVs. The parameter update rules and stability conditions of the learning are derived, and the proof of the stability of the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the applicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to time-varying wind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time control systems because of its computational efficiency.

  17. Application of adaptive fuzzy control technology to pressure control of a pressurizer

    Institute of Scientific and Technical Information of China (English)

    YANG Ben-kun; BIAN Xin-qian; GUO Wei-lai

    2005-01-01

    A pressurizer is one of important equipment in a pressurized water reactor plant. It is used to maintain the pressure of primary coolant within allowed range because the sharp change of coolant pressure affects the security of reactor,therefor,the study of pressurizer's pressure control methods is very important. In this paper, an adaptive fuzzy controller is presented for pressure control of a pressurizer in a nuclear power plant. The controller can on-line tune fuzzy control rules and parameters by self-learning in the actual control process, which possesses the way of thinking like human to make a decision. The simulation results for a pressurized water reactor plant show that the adaptive fuzzy controller has optimum and intelligent characteristics, which prove the controller is effective.

  18. Place preference and vocal learning rely on distinct reinforcers in songbirds.

    Science.gov (United States)

    Murdoch, Don; Chen, Ruidong; Goldberg, Jesse H

    2018-04-30

    In reinforcement learning (RL) agents are typically tasked with maximizing a single objective function such as reward. But it remains poorly understood how agents might pursue distinct objectives at once. In machines, multiobjective RL can be achieved by dividing a single agent into multiple sub-agents, each of which is shaped by agent-specific reinforcement, but it remains unknown if animals adopt this strategy. Here we use songbirds to test if navigation and singing, two behaviors with distinct objectives, can be differentially reinforced. We demonstrate that strobe flashes aversively condition place preference but not song syllables. Brief noise bursts aversively condition song syllables but positively reinforce place preference. Thus distinct behavior-generating systems, or agencies, within a single animal can be shaped by correspondingly distinct reinforcement signals. Our findings suggest that spatially segregated vocal circuits can solve a credit assignment problem associated with multiobjective learning.

  19. Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems

    Energy Technology Data Exchange (ETDEWEB)

    Jassar, S.; Zhao, L. [Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON (Canada); Liao, Z. [Department of Architectural Science, Ryerson University (Canada)

    2009-08-15

    The heating systems are conventionally controlled by open-loop control systems because of the absence of practical methods for estimating average air temperature in the built environment. An inferential sensor model, based on adaptive neuro-fuzzy inference system modeling, for estimating the average air temperature in multi-zone space heating systems is developed. This modeling technique has the advantage of expert knowledge of fuzzy inference systems (FISs) and learning capability of artificial neural networks (ANNs). A hybrid learning algorithm, which combines the least-square method and the back-propagation algorithm, is used to identify the parameters of the network. This paper describes an adaptive network based inferential sensor that can be used to design closed-loop control for space heating systems. The research aims to improve the overall performance of heating systems, in terms of energy efficiency and thermal comfort. The average air temperature results estimated by using the developed model are strongly in agreement with the experimental results. (author)

  20. Functional Based Adaptive and Fuzzy Sliding Controller for Non-Autonomous Active Suspension System

    Science.gov (United States)

    Huang, Shiuh-Jer; Chen, Hung-Yi

    In this paper, an adaptive sliding controller is developed for controlling a vehicle active suspension system. The functional approximation technique is employed to substitute the unknown non-autonomous functions of the suspension system and release the model-based requirement of sliding mode control algorithm. In order to improve the control performance and reduce the implementation problem, a fuzzy strategy with online learning ability is added to compensate the functional approximation error. The update laws of the functional approximation coefficients and the fuzzy tuning parameters are derived from the Lyapunov theorem to guarantee the system stability. The proposed controller is implemented on a quarter-car hydraulic actuating active suspension system test-rig. The experimental results show that the proposed controller suppresses the oscillation amplitude of the suspension system effectively.

  1. Prediction of monthly electric energy consumption using pattern-based fuzzy nearest neighbour regression

    Directory of Open Access Journals (Sweden)

    Pełka Paweł

    2017-01-01

    Full Text Available Electricity demand forecasting is of important role in power system planning and operation. In this work, fuzzy nearest neighbour regression has been utilised to estimate monthly electricity demands. The forecasting model was based on the pre-processed energy consumption time series, where input and output variables were defined as patterns representing unified fragments of the time series. Relationships between inputs and outputs, which were simplified due to patterns, were modelled using nonparametric regression with weighting function defined as a fuzzy membership of learning points to the neighbourhood of a query point. In an experimental part of the work the model was evaluated using real-world data. The results are encouraging and show high performances of the model and its competitiveness compared to other forecasting models.

  2. Fuzzy delay model based fault simulator for crosstalk delay fault test ...

    Indian Academy of Sciences (India)

    In this paper, a fuzzy delay model based crosstalk delay fault simulator is proposed. As design .... To find the quality of non-robust tests, a fuzzy delay ..... Dubois D and Prade H 1989 Processing Fuzzy temporal knowledge. IEEE Transactions ...

  3. Adaptive Robust Online Constructive Fuzzy Control of a Complex Surface Vehicle System.

    Science.gov (United States)

    Wang, Ning; Er, Meng Joo; Sun, Jing-Chao; Liu, Yan-Cheng

    2016-07-01

    In this paper, a novel adaptive robust online constructive fuzzy control (AR-OCFC) scheme, employing an online constructive fuzzy approximator (OCFA), to deal with tracking surface vehicles with uncertainties and unknown disturbances is proposed. Significant contributions of this paper are as follows: 1) unlike previous self-organizing fuzzy neural networks, the OCFA employs decoupled distance measure to dynamically allocate discriminable and sparse fuzzy sets in each dimension and is able to parsimoniously self-construct high interpretable T-S fuzzy rules; 2) an OCFA-based dominant adaptive controller (DAC) is designed by employing the improved projection-based adaptive laws derived from the Lyapunov synthesis which can guarantee reasonable fuzzy partitions; 3) closed-loop system stability and robustness are ensured by stable cancelation and decoupled adaptive compensation, respectively, thereby contributing to an auxiliary robust controller (ARC); and 4) global asymptotic closed-loop system can be guaranteed by AR-OCFC consisting of DAC and ARC and all signals are bounded. Simulation studies and comprehensive comparisons with state-of-the-arts fixed- and dynamic-structure adaptive control schemes demonstrate superior performance of the AR-OCFC in terms of tracking and approximation accuracy.

  4. A design-based approach with vocational teachers to promote self-regulated learning

    NARCIS (Netherlands)

    Jossberger, Helen; Brand-Gruwel, Saskia; Van de Wiel, Margje; Boshuizen, Els

    2011-01-01

    Jossberger, H., Brand-Gruwel, S., Van de Wiel, M., & Boshuizen, H. P. A. (2011, August). A design-based approach with vocational teachers to promote self-regulated learning. Presentation at the 14th European Conference for Research on Learning and Instruction (EARLI), Exeter, England.

  5. Fuzzy promises

    DEFF Research Database (Denmark)

    Anker, Thomas Boysen; Kappel, Klemens; Eadie, Douglas

    2012-01-01

    as narrative material to communicate self-identity. Finally, (c) we propose that brands deliver fuzzy experiential promises through effectively motivating consumers to adopt and play a social role implicitly suggested and facilitated by the brand. A promise is an inherently ethical concept and the article...... concludes with an in-depth discussion of fuzzy brand promises as two-way ethical commitments that put requirements on both brands and consumers....

  6. Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function

    Directory of Open Access Journals (Sweden)

    Jian Shi

    2016-11-01

    Full Text Available Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied in the fields of statistics and machine learning. In this paper a novel fuzzy support vector machine (SVM credit scoring model is proposed for credit risk analysis, in which fuzzy membership is adopted to indicate different contribution of each input point to the learning of SVM classification hyperplane. Considering the methodological consistency, support vector data description (SVDD is introduced to construct the fuzzy membership function and to reduce the effect of outliers and noises. The SVDD-based fuzzy SVM model is tested against the traditional fuzzy SVM on two real-world datasets and the research results confirm the effectiveness of the presented method.

  7. Technology and Development of Self-Reinforced Polymer Composites

    Science.gov (United States)

    Alcock, Ben; Peijs, Ton

    In recent years there has been an increasing amount of interest, both commercially and scientifically, in the emerging field of "self-reinforced polymer composites". These materials, which are sometimes also referred to as "single polymer composites", or "all-polymer composites", were first conceived in the 1970s, and are now beginning to appear in a range of commercial products. While high mechanical performance polymer fibres or tapes are an obvious precursor for composite development, various different technologies have been developed to consolidate these into two- or three-dimensional structures. This paper presents a review of the various processing techniques that have been reported in the literature for the manufacture of self-reinforced polymer composites from fibres or tapes of different polymers, and so exploit the fibre or tape performance in a commercial material or product.

  8. Aggregation Operator Based Fuzzy Pattern Classifier Design

    DEFF Research Database (Denmark)

    Mönks, Uwe; Larsen, Henrik Legind; Lohweg, Volker

    2009-01-01

    This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable....... The performances of novel classifiers using substitutes of MFPC's geometric mean aggregator are benchmarked in the scope of an image processing application against the MFPC to reveal classification improvement potentials for obtaining higher classification rates....

  9. Spoken language identification based on the enhanced self-adjusting extreme learning machine approach

    Science.gov (United States)

    Tiun, Sabrina; AL-Dhief, Fahad Taha; Sammour, Mahmoud A. M.

    2018-01-01

    Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based on literature, is a mature process where the standard features for LID have already been developed using Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), the Gaussian Mixture Model (GMM) and ending with the i-vector based framework. However, the process of learning based on extract features remains to be improved (i.e. optimised) to capture all embedded knowledge on the extracted features. The Extreme Learning Machine (ELM) is an effective learning model used to perform classification and regression analysis and is extremely useful to train a single hidden layer neural network. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. In this study, the ELM is selected as a learning model for LID based on standard feature extraction. One of the optimisation approaches of ELM, the Self-Adjusting Extreme Learning Machine (SA-ELM) is selected as the benchmark and improved by altering the selection phase of the optimisation process. The selection process is performed incorporating both the Split-Ratio and K-Tournament methods, the improved SA-ELM is named Enhanced Self-Adjusting Extreme Learning Machine (ESA-ELM). The results are generated based on LID with the datasets created from eight different languages. The results of the study showed excellent superiority relating to the performance of the Enhanced Self-Adjusting Extreme Learning Machine LID (ESA-ELM LID) compared with the SA-ELM LID, with ESA-ELM LID achieving an accuracy of 96.25%, as compared to the accuracy of SA-ELM LID of only 95.00%. PMID:29672546

  10. Spoken language identification based on the enhanced self-adjusting extreme learning machine approach.

    Science.gov (United States)

    Albadr, Musatafa Abbas Abbood; Tiun, Sabrina; Al-Dhief, Fahad Taha; Sammour, Mahmoud A M

    2018-01-01

    Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based on literature, is a mature process where the standard features for LID have already been developed using Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), the Gaussian Mixture Model (GMM) and ending with the i-vector based framework. However, the process of learning based on extract features remains to be improved (i.e. optimised) to capture all embedded knowledge on the extracted features. The Extreme Learning Machine (ELM) is an effective learning model used to perform classification and regression analysis and is extremely useful to train a single hidden layer neural network. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. In this study, the ELM is selected as a learning model for LID based on standard feature extraction. One of the optimisation approaches of ELM, the Self-Adjusting Extreme Learning Machine (SA-ELM) is selected as the benchmark and improved by altering the selection phase of the optimisation process. The selection process is performed incorporating both the Split-Ratio and K-Tournament methods, the improved SA-ELM is named Enhanced Self-Adjusting Extreme Learning Machine (ESA-ELM). The results are generated based on LID with the datasets created from eight different languages. The results of the study showed excellent superiority relating to the performance of the Enhanced Self-Adjusting Extreme Learning Machine LID (ESA-ELM LID) compared with the SA-ELM LID, with ESA-ELM LID achieving an accuracy of 96.25%, as compared to the accuracy of SA-ELM LID of only 95.00%.

  11. Pleasurable music affects reinforcement learning according to the listener

    Science.gov (United States)

    Gold, Benjamin P.; Frank, Michael J.; Bogert, Brigitte; Brattico, Elvira

    2013-01-01

    Mounting evidence links the enjoyment of music to brain areas implicated in emotion and the dopaminergic reward system. In particular, dopamine release in the ventral striatum seems to play a major role in the rewarding aspect of music listening. Striatal dopamine also influences reinforcement learning, such that subjects with greater dopamine efficacy learn better to approach rewards while those with lesser dopamine efficacy learn better to avoid punishments. In this study, we explored the practical implications of musical pleasure through its ability to facilitate reinforcement learning via non-pharmacological dopamine elicitation. Subjects from a wide variety of musical backgrounds chose a pleasurable and a neutral piece of music from an experimenter-compiled database, and then listened to one or both of these pieces (according to pseudo-random group assignment) as they performed a reinforcement learning task dependent on dopamine transmission. We assessed musical backgrounds as well as typical listening patterns with the new Helsinki Inventory of Music and Affective Behaviors (HIMAB), and separately investigated behavior for the training and test phases of the learning task. Subjects with more musical experience trained better with neutral music and tested better with pleasurable music, while those with less musical experience exhibited the opposite effect. HIMAB results regarding listening behaviors and subjective music ratings indicate that these effects arose from different listening styles: namely, more affective listening in non-musicians and more analytical listening in musicians. In conclusion, musical pleasure was able to influence task performance, and the shape of this effect depended on group and individual factors. These findings have implications in affective neuroscience, neuroaesthetics, learning, and music therapy. PMID:23970875

  12. AUTOMATIC MULTILEVEL IMAGE SEGMENTATION BASED ON FUZZY REASONING

    Directory of Open Access Journals (Sweden)

    Liang Tang

    2011-05-01

    Full Text Available An automatic multilevel image segmentation method based on sup-star fuzzy reasoning (SSFR is presented. Using the well-known sup-star fuzzy reasoning technique, the proposed algorithm combines the global statistical information implied in the histogram with the local information represented by the fuzzy sets of gray-levels, and aggregates all the gray-levels into several classes characterized by the local maximum values of the histogram. The presented method has the merits of determining the number of the segmentation classes automatically, and avoiding to calculating thresholds of segmentation. Emulating and real image segmentation experiments demonstrate that the SSFR is effective.

  13. Modelling and Analysis of Automobile Vibration System Based on Fuzzy Theory under Different Road Excitation Information

    Directory of Open Access Journals (Sweden)

    Xue-wen Chen

    2018-01-01

    Full Text Available A fuzzy increment controller is designed aimed at the vibration system of automobile active suspension with seven degrees of freedom (DOF. For decreasing vibration, an active control force is acquired by created Proportion-Integration-Differentiation (PID controller. The controller’s parameters are adjusted by a fuzzy increment controller with self-modifying parameters functions, which adopts the deviation and its rate of change of the body’s vertical vibration velocity and the desired value in the position of the front and rear suspension as the input variables based on 49 fuzzy control rules. Adopting Simulink, the fuzzy increment controller is validated under different road excitation, such as the white noise input with four-wheel correlation in time-domain, the sinusoidal input, and the pulse input of C-grade road surface. The simulation results show that the proposed controller can reduce obviously the vehicle vibration compared to other independent control types in performance indexes, such as, the root mean square value of the body’s vertical vibration acceleration, pitching, and rolling angular acceleration.

  14. IMPLEMENTATION OF MULTIAGENT REINFORCEMENT LEARNING MECHANISM FOR OPTIMAL ISLANDING OPERATION OF DISTRIBUTION NETWORK

    DEFF Research Database (Denmark)

    Saleem, Arshad; Lind, Morten

    2008-01-01

    among electric power utilities to utilize modern information and communication technologies (ICT) in order to improve the automation of the distribution system. In this paper we present our work for the implementation of a dynamic multi-agent based distributed reinforcement learning mechanism...

  15. An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation.

    Directory of Open Access Journals (Sweden)

    Huu-Tho Nguyen

    Full Text Available Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process and a fuzzy COmplex PRoportional ASsessment (COPRAS for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.

  16. Self-reinforcement and protein sustained delivery of hyaluronan hydrogel by tailoring a dually cross-linked network

    Energy Technology Data Exchange (ETDEWEB)

    Luo, Chunhong; Xu, Guoguang; Wang, Xinghui [Department of Materials Science and Engineering, College of Science and Engineering, Jinan University, Guangzhou 510632 (China); Tu, Mei; Zeng, Rong; Rong, Jianhua [Department of Materials Science and Engineering, College of Science and Engineering, Jinan University, Guangzhou 510632 (China); Engineering Research Center of Artificial Organs and Materials, Ministry of Education, Guangzhou 510632 (China); Zhao, Jianhao, E-mail: jhzhao@jnu.edu.cn [Department of Materials Science and Engineering, College of Science and Engineering, Jinan University, Guangzhou 510632 (China); Engineering Research Center of Artificial Organs and Materials, Ministry of Education, Guangzhou 510632 (China)

    2015-01-01

    A series of self-reinforcing hyaluronan hydrogels were developed to improve mechanical properties and protein sustained delivery thanks to a dually cross-linked network. Hyaluronan gel particles (HGPs, 1–5 μm in diameter) with different cross-linking densities, i.e. HGPs-1.5, HGPs-3 and HGPs-15, were prepared in an inverse emulsion system and used as the reinforcing phase after glycidyl methacrylation, while glycidyl methacrylated hyaluronan with a substitution degree of 45.2% was synthesized as the matrix phase. These two phases were cross-linked under ultraviolet irradiation to form self-reinforcing hyaluronan hydrogels (srHAs) that showed typical cross-linked structure of HGPs connecting the matrix phase by cross-section observation. In comparison to hyaluronan bulk gels and their blends with HGPs, srHAs distinctly enhanced the mechanical properties and BSA long-term sustained delivery, especially srHA-1.5 showed the highest compressive modulus of 220 ± 15 kPa and the slowest BSA delivery (67% release at 14 d). The 3T3 fibroblast cell culture showed that all the srHAs had no cytotoxicity. - Highlights: • New self-reinforcing HA hydrogels with a dually cross-linked network were developed. • Self-reinforcing HA hydrogels greatly enhanced the mechanical properties. • Self-reinforcing HA hydrogels prolonged the sustained delivery of BSA. • The self-reinforcing mechanism and BSA diffusion mechanism were discussed. • Self-reinforcing HA hydrogels had no cytotoxicity to 3T3 fibroblast cells.

  17. Self-reinforcement and protein sustained delivery of hyaluronan hydrogel by tailoring a dually cross-linked network

    International Nuclear Information System (INIS)

    Luo, Chunhong; Xu, Guoguang; Wang, Xinghui; Tu, Mei; Zeng, Rong; Rong, Jianhua; Zhao, Jianhao

    2015-01-01

    A series of self-reinforcing hyaluronan hydrogels were developed to improve mechanical properties and protein sustained delivery thanks to a dually cross-linked network. Hyaluronan gel particles (HGPs, 1–5 μm in diameter) with different cross-linking densities, i.e. HGPs-1.5, HGPs-3 and HGPs-15, were prepared in an inverse emulsion system and used as the reinforcing phase after glycidyl methacrylation, while glycidyl methacrylated hyaluronan with a substitution degree of 45.2% was synthesized as the matrix phase. These two phases were cross-linked under ultraviolet irradiation to form self-reinforcing hyaluronan hydrogels (srHAs) that showed typical cross-linked structure of HGPs connecting the matrix phase by cross-section observation. In comparison to hyaluronan bulk gels and their blends with HGPs, srHAs distinctly enhanced the mechanical properties and BSA long-term sustained delivery, especially srHA-1.5 showed the highest compressive modulus of 220 ± 15 kPa and the slowest BSA delivery (67% release at 14 d). The 3T3 fibroblast cell culture showed that all the srHAs had no cytotoxicity. - Highlights: • New self-reinforcing HA hydrogels with a dually cross-linked network were developed. • Self-reinforcing HA hydrogels greatly enhanced the mechanical properties. • Self-reinforcing HA hydrogels prolonged the sustained delivery of BSA. • The self-reinforcing mechanism and BSA diffusion mechanism were discussed. • Self-reinforcing HA hydrogels had no cytotoxicity to 3T3 fibroblast cells

  18. Application of a Reinforced Self-Compacting Concrete Jacket in Damaged Reinforced Concrete Beams under Monotonic and Repeated Loading

    Directory of Open Access Journals (Sweden)

    Constantin E. Chalioris

    2013-01-01

    Full Text Available This paper presents the findings of an experimental study on the application of a reinforced self-compacting concrete jacketing technique in damaged reinforced concrete beams. Test results of 12 specimens subjected to monotonic loading up to failure or under repeated loading steps prior to total failure are included. First, 6 beams were designed to be shear dominated, constructed by commonly used concrete, were initially tested, damaged, and failed in a brittle manner. Afterwards, the shear-damaged beams were retrofitted using a self-compacting concrete U-formed jacket that consisted of small diameter steel bars and U-formed stirrups in order to increase their shear resistance and potentially to alter their initially observed shear response to a more ductile one. The jacketed beams were retested under the same loading. Test results indicated that the application of reinforced self-compacting concrete jacketing in damaged reinforced concrete beams is a promising rehabilitation technique. All the jacketed beams showed enhanced overall structural response and 35% to 50% increased load bearing capacities. The ultimate shear load of the jacketed beams varied from 39.7 to 42.0 kN, whereas the capacity of the original beams was approximately 30% lower. Further, all the retrofitted specimens exhibited typical flexural response with high values of deflection ductility.

  19. The Relative Effect of Team-Based Learning on Motivation and Learning: A Self-Determination Theory Perspective

    Science.gov (United States)

    Jeno, Lucas M.; Raaheim, Arild; Kristensen, Sara Madeleine; Kristensen, Kjell Daniel; Hole, Torstein Nielsen; Haugland, Mildrid J.; Mæland, Silje

    2017-01-01

    We investigate the effects of team-based learning (TBL) on motivation and learning in a quasi-experimental study. The study employs a self-determination theory perspective to investigate the motivational effects of implementing TBL in a physiotherapy course in higher education. We adopted a one-group pretest-posttest design. The results show that…

  20. In situ development of self-reinforced cellulose nanocrystals based thermoplastic elastomers by atom transfer radical polymerization.

    Science.gov (United States)

    Yu, Juan; Wang, Chunpeng; Wang, Jifu; Chu, Fuxiang

    2016-05-05

    Recently, the utilization of cellulose nanocrystals (CNCs) as a reinforcing material has received a great attention due to its high elastic modulus. In this article, a novel strategy for the synthesis of self-reinforced CNCs based thermoplastic elastomers (CTPEs) is presented. CNCs were first surface functionalized with an initiator for surface-initiated atom transfer radical polymerization (SI-ATRP). Subsequently, SI-ATRP of methyl methacrylate (MMA) and butyl acrylate (BA) was carried out in the presence of sacrificial initiator to form CTPEs in situ. The CTPEs together with the simple blends of CNCs and linear poly(MMA-co-BA) copolymer (P(MMA-co-BA)) were characterized for comparative study. The results indicated that P(MMA-co-BA) was successfully grafted onto the surface of CNCs and the compatibility between CNCs and the polymer matrix in CTPEs was greatly enhanced. Specially, the CTPEs containing 2.15wt% CNCs increased Tg by 19.2°C and tensile strength by 100% as compared to the linear P(MMA-co-BA). Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Stability Analysis of Interconnected Fuzzy Systems Using the Fuzzy Lyapunov Method

    Directory of Open Access Journals (Sweden)

    Ken Yeh

    2010-01-01

    Full Text Available The fuzzy Lyapunov method is investigated for use with a class of interconnected fuzzy systems. The interconnected fuzzy systems consist of J interconnected fuzzy subsystems, and the stability analysis is based on Lyapunov functions. Based on traditional Lyapunov stability theory, we further propose a fuzzy Lyapunov method for the stability analysis of interconnected fuzzy systems. The fuzzy Lyapunov function is defined in fuzzy blending quadratic Lyapunov functions. Some stability conditions are derived through the use of fuzzy Lyapunov functions to ensure that the interconnected fuzzy systems are asymptotically stable. Common solutions can be obtained by solving a set of linear matrix inequalities (LMIs that are numerically feasible. Finally, simulations are performed in order to verify the effectiveness of the proposed stability conditions in this paper.

  2. Intuitionistic fuzzy-based model for failure detection.

    Science.gov (United States)

    Aikhuele, Daniel O; Turan, Faiz B M

    2016-01-01

    In identifying to-be-improved product component(s), the customer/user requirements which are mainly considered, and achieved through customer surveys using the quality function deployment (QFD) tool, often fail to guarantee or cover aspects of the product reliability. Even when they do, there are always many misunderstandings. To improve the product reliability and quality during product redesigning phase and to create that novel product(s) for the customers, the failure information of the existing product, and its component(s) should ordinarily be analyzed and converted to appropriate design knowledge for the design engineer. In this paper, a new intuitionistic fuzzy multi-criteria decision-making method has been proposed. The new approach which is based on an intuitionistic fuzzy TOPSIS model uses an exponential-related function for the computation of the separation measures from the intuitionistic fuzzy positive ideal solution (IFPIS) and intuitionistic fuzzy negative ideal solution (IFNIS) of alternatives. The proposed method has been applied to two practical case studies, and the result from the different cases has been compared with some similar computational approaches in the literature.

  3. Shake-table testing of a self-centering precast reinforced concrete frame with shear walls

    Science.gov (United States)

    Lu, Xilin; Yang, Boya; Zhao, Bin

    2018-04-01

    The seismic performance of a self-centering precast reinforced concrete (RC) frame with shear walls was investigated in this paper. The lateral force resistance was provided by self-centering precast RC shear walls (SPCW), which utilize a combination of unbonded prestressed post-tensioned (PT) tendons and mild steel reinforcing bars for flexural resistance across base joints. The structures concentrated deformations at the bottom joints and the unbonded PT tendons provided the self-centering restoring force. A 1/3-scale model of a five-story self-centering RC frame with shear walls was designed and tested on a shake-table under a series of bi-directional earthquake excitations with increasing intensity. The acceleration response, roof displacement, inter-story drifts, residual drifts, shear force ratios, hysteresis curves, and local behaviour of the test specimen were analysed and evaluated. The results demonstrated that seismic performance of the test specimen was satisfactory in the plane of the shear wall; however, the structure sustained inter-story drift levels up to 2.45%. Negligible residual drifts were recorded after all applied earthquake excitations. Based on the shake-table test results, it is feasible to apply and popularize a self-centering precast RC frame with shear walls as a structural system in seismic regions.

  4. An Analysis of Stochastic Game Theory for Multiagent Reinforcement Learning

    National Research Council Canada - National Science Library

    Bowling, Michael

    2000-01-01

    .... In this paper we contribute a comprehensive presentation of the relevant techniques for solving stochastic games from both the game theory community and reinforcement learning communities. We examine the assumptions and limitations of these algorithms, and identify similarities between these algorithms, single agent reinforcement learners, and basic game theory techniques.

  5. Adaptive complementary fuzzy self-recurrent wavelet neural network controller for the electric load simulator system

    Directory of Open Access Journals (Sweden)

    Wang Chao

    2016-03-01

    Full Text Available Due to the complexities existing in the electric load simulator, this article develops a high-performance nonlinear adaptive controller to improve the torque tracking performance of the electric load simulator, which mainly consists of an adaptive fuzzy self-recurrent wavelet neural network controller with variable structure (VSFSWC and a complementary controller. The VSFSWC is clearly and easily used for real-time systems and greatly improves the convergence rate and control precision. The complementary controller is designed to eliminate the effect of the approximation error between the proposed neural network controller and the ideal feedback controller without chattering phenomena. Moreover, adaptive learning laws are derived to guarantee the system stability in the sense of the Lyapunov theory. Finally, the hardware-in-the-loop simulations are carried out to verify the feasibility and effectiveness of the proposed algorithms in different working styles.

  6. The roles of social factor and internet self-efficacy in nurses' web-based continuing learning.

    Science.gov (United States)

    Chiu, Yen-Lin; Tsai, Chin-Chung

    2014-03-01

    This study was conducted to explore the relationships among social factor, Internet self-efficacy and attitudes toward web-based continuing learning in a clinical nursing setting. The participants recruited were 244 in-service nurses from hospitals in Taiwan. Three instruments were used to assess their perceptions of social factor, Internet self-efficacy (including basic and advanced Internet self-efficacy) and attitudes toward web-based continuing learning (including perceived usefulness, perceived ease of use, affection and behavior). Structural equation modeling (SEM) was utilized to identify the hypothesized structural model. The results of this study support that social factor is a significant factor correlated to Internet self-efficacy and attitudes toward web-based continuing learning (including perceived usefulness, perceived ease of use and affection). In addition, nurses' basic Internet self-efficacy plays a key role in attitudes including perceived usefulness, perceived ease of use and affection. However, advanced self-efficacy was not correlated to any of the attitudes. The behavior dimension was not linked to social factor or Internet self-efficacy, but was linked to perceived ease of use and affection. Copyright © 2013 Elsevier Ltd. All rights reserved.

  7. Foundations Of Fuzzy Control

    DEFF Research Database (Denmark)

    Jantzen, Jan

    The objective of this textbook is to acquire an understanding of the behaviour of fuzzy logic controllers. Under certain conditions a fuzzy controller is equivalent to a proportional-integral-derivative (PID) controller. Using that equivalence as a link, the book applies analysis methods from...... linear and nonlinear control theory. In the linear domain, PID tuning methods and stability analyses are transferred to linear fuzzy controllers. The Nyquist plot shows the robustness of different settings of the fuzzy gain parameters. As a result, a fuzzy controller is guaranteed to perform as well...... as any PID controller. In the nonlinear domain, the stability of four standard control surfaces is analysed by means of describing functions and Nyquist plots. The self-organizing controller (SOC) is shown to be a model reference adaptive controller. There is a possibility that a nonlinear fuzzy PID...

  8. Web Interface Design Principles for Adults’ Self-Directed Learning

    Directory of Open Access Journals (Sweden)

    Mehmet FIRAT

    2016-10-01

    Full Text Available One of the most important features which e-learning tools and environments must possess within the scope of lifelong learning is self-directed learning, which can be considered as a form of self-learning. The aim of this study was to determine, based on the views and recommendations of experts, interface design principles for the development of educational web interfaces that will support the self-directed learning of adults. This descriptive study was conducted with the contribution of 12 academicians specializing in interface design and self-directed learning. Within the scope of the study, new interfaces features were identified based on an evaluation of the literature on interface designs for self-directed learning, and the views of subject experts. Based on the study results, it was determined that interface designs supporting self-directed learning must possess five basic features, which include being user-directed, ensuring variety, being supported by learning analytics, being motivational, and being sharing-oriented.

  9. Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

    Science.gov (United States)

    Mahmoudi, Babak; Pohlmeyer, Eric A; Prins, Noeline W; Geng, Shijia; Sanchez, Justin C

    2013-12-01

    Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

  10. Robust modified GA based multi-stage fuzzy LFC

    International Nuclear Information System (INIS)

    Shayeghi, H.; Jalili, A.; Shayanfar, H.A.

    2007-01-01

    In this paper, a robust genetic algorithm (GA) based multi-stage fuzzy (MSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operates under deregulation based on the bilateral policy scheme. In this strategy, the control signal is tuned online from the knowledge base and the fuzzy inference, which request fewer sources and has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of the membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by modified genetic algorithms. The classical genetic algorithms are powerful search techniques to find the global optimal area. However, the global optimum value is not guaranteed using this method, and the speed of the algorithm's convergence is extremely reduced too. To overcome this drawback, a modified genetic algorithm is being used to tune the membership functions of the proposed MSF controller. The effectiveness of the proposed method is demonstrated on a three area restructured power system with possible contracted scenarios under large load demand and area disturbances in comparison with the multi-stage fuzzy and classical fuzzy PID controllers through FD and ITAE performance indices. The results evaluation shows that the proposed control strategy achieves good robust performance for a wide range of system parameters and load changes in the presence of system nonlinearities and is superior to the other controllers. Moreover, this newly developed control strategy has a simple structure, does not require an accurate model of the plant and is fairly easy to implement, which can be useful for the real world complex power systems

  11. Robust modified GA based multi-stage fuzzy LFC

    Energy Technology Data Exchange (ETDEWEB)

    Shayeghi, H. [Technical Engineering Department, The University of Mohaghegh Ardebili, Daneshkah St., Ardebil (Iran); Jalili, A. [Electrical Engineering Group, Islamic Azad University, Ardebil Branch, Ardebil (Iran); Shayanfar, H.A. [Electrical Engineering Department, Iran University of Science and Technology, Tehran (Iran)

    2007-05-15

    In this paper, a robust genetic algorithm (GA) based multi-stage fuzzy (MSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operates under deregulation based on the bilateral policy scheme. In this strategy, the control signal is tuned online from the knowledge base and the fuzzy inference, which request fewer sources and has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of the membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by modified genetic algorithms. The classical genetic algorithms are powerful search techniques to find the global optimal area. However, the global optimum value is not guaranteed using this method, and the speed of the algorithm's convergence is extremely reduced too. To overcome this drawback, a modified genetic algorithm is being used to tune the membership functions of the proposed MSF controller. The effectiveness of the proposed method is demonstrated on a three area restructured power system with possible contracted scenarios under large load demand and area disturbances in comparison with the multi-stage fuzzy and classical fuzzy PID controllers through FD and ITAE performance indices. The results evaluation shows that the proposed control strategy achieves good robust performance for a wide range of system parameters and load changes in the presence of system nonlinearities and is superior to the other controllers. Moreover, this newly developed control strategy has a simple structure, does not require an accurate model of the plant and is fairly easy to implement, which can be useful for the real world complex power systems. (author)

  12. Neurofeedback in Learning Disabled Children: Visual versus Auditory Reinforcement.

    Science.gov (United States)

    Fernández, Thalía; Bosch-Bayard, Jorge; Harmony, Thalía; Caballero, María I; Díaz-Comas, Lourdes; Galán, Lídice; Ricardo-Garcell, Josefina; Aubert, Eduardo; Otero-Ojeda, Gloria

    2016-03-01

    Children with learning disabilities (LD) frequently have an EEG characterized by an excess of theta and a deficit of alpha activities. NFB using an auditory stimulus as reinforcer has proven to be a useful tool to treat LD children by positively reinforcing decreases of the theta/alpha ratio. The aim of the present study was to optimize the NFB procedure by comparing the efficacy of visual (with eyes open) versus auditory (with eyes closed) reinforcers. Twenty LD children with an abnormally high theta/alpha ratio were randomly assigned to the Auditory or the Visual group, where a 500 Hz tone or a visual stimulus (a white square), respectively, was used as a positive reinforcer when the value of the theta/alpha ratio was reduced. Both groups had signs consistent with EEG maturation, but only the Auditory Group showed behavioral/cognitive improvements. In conclusion, the auditory reinforcer was more efficacious in reducing the theta/alpha ratio, and it improved the cognitive abilities more than the visual reinforcer.

  13. Inovasi Pengembangan Metode Pembelajaran Dengan Menggunakan Real Avatar-Based Learning Dalam Pendidikan Keperawatan: A Bridge Connection Theory and Practice di STIKEP PPNI Jawa Barat

    Directory of Open Access Journals (Sweden)

    Linlin Lindayani

    2017-11-01

    Full Text Available Virtual learning is one of the most effective and efficient learning methods, especially in improving skills including soft skills. In Indonesia, the problem-based learning methodology (PBL is the most widely applied but has weaknesses in helping to bridge the students in the application of theory to practice. The purpose of this research was to develop learning method by using avatar-based learning to self-directed learning, which is one of the main competencies of nursing education that is lifelong learning. This study was quasi experiment with one group of intervention. Respondents in this study were nursing students of stratum 1 level four. The Self-Directed Learning Instrument (SDLI was used to measure this research outcome. The paired t-test was used to evaluate the effectiveness of this method against outcomes. A total of 40 students agreed to participate in the study. Before intervention, the mean score for the total self-directed learning score was 72.3 (SD = 8.97. Based on the results of paired t-test about the effectiveness of real-avatar-based learning on self-directed learning, was found that after applied real-avatar based-learning for Medical Surgical Nursing III course there were an increasing of the self-directed learning (different value = 4.56, p value = 0.001. Learning method by applying real avatar-based learning was effective in improve student’s self-directed learning especially on the aspect of improvement of planning, implementation and self-monitoring. For further research, using more rigors with other outcomes is needed to reinforce the effectiveness of this method

  14. Fuzzy AutoEncode Based Cloud Detection for Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Zhenfeng Shao

    2017-03-01

    Full Text Available Cloud detection of remote sensing imagery is quite challenging due to the influence of complicated underlying surfaces and the variety of cloud types. Currently, most of the methods mainly rely on prior knowledge to extract features artificially for cloud detection. However, these features may not be able to accurately represent the cloud characteristics under complex environment. In this paper, we adopt an innovative model named Fuzzy Autoencode Model (FAEM to integrate the feature learning ability of stacked autoencode networks and the detection ability of fuzzy function for highly accurate cloud detection on remote sensing imagery. Our proposed method begins by selecting and fusing spectral, texture, and structure information. Thereafter, the proposed technique established a FAEM to learn the deep discriminative features from a great deal of selected information. Finally, the learned features are mapped to the corresponding cloud density map with a fuzzy function. To demonstrate the effectiveness of the proposed method, 172 Landsat ETM+ images and 25 GF-1 images with different spatial resolutions are used in this paper. For the convenience of accuracy assessment, ground truth data are manually outlined. Results show that the average RER (ratio of right rate and error rate on Landsat images is greater than 29, while the average RER of Support Vector Machine (SVM is 21.8 and Random Forest (RF is 23. The results on GF-1 images exhibit similar performance as Landsat images with the average RER of 25.9, which is much higher than the results of SVM and RF. Compared to traditional methods, our technique has attained higher average cloud detection accuracy for either different spatial resolutions or various land surfaces.

  15. Automatic detection of multiple UXO-like targets using magnetic anomaly inversion and self-adaptive fuzzy c-means clustering

    Science.gov (United States)

    Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoquan; Li, Zhining

    2017-12-01

    We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.

  16. Transporting fibres as reinforcement in self-compacting concrete

    NARCIS (Netherlands)

    Grünewald, S.; Walraven, J.C.

    2009-01-01

    The development of self-compacting concrete (SCC) was an important step towards efficiency at building sites, rationally producing prefabricated concrete elements, better working conditions and improved quality and appearance of concrete structures. By adding fibres to SCC bar reinforcement can be

  17. Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments

    Science.gov (United States)

    Tran, Loc; Cross, Charles; Montague, Gilbert; Motter, Mark; Neilan, James; Qualls, Garry; Rothhaar, Paul; Trujillo, Anna; Allen, B. Danette

    2015-01-01

    We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

  18. Intranasal oxytocin enhances socially-reinforced learning in rhesus monkeys

    Directory of Open Access Journals (Sweden)

    Lisa A Parr

    2014-09-01

    Full Text Available There are currently no drugs approved for the treatment of social deficits associated with autism spectrum disorders (ASD. One hypothesis for these deficits is that individuals with ASD lack the motivation to attend to social cues because those cues are not implicitly rewarding. Therefore, any drug that could enhance the rewarding quality of social stimuli could have a profound impact on the treatment of ASD, and other social disorders. Oxytocin (OT is a neuropeptide that has been effective in enhancing social cognition and social reward in humans. The present study examined the ability of OT to selectively enhance learning after social compared to nonsocial reward in rhesus monkeys, an important species for modeling the neurobiology of social behavior in humans. Monkeys were required to learn an implicit visual matching task after receiving either intranasal (IN OT or Placebo (saline. Correct trials were rewarded with the presentation of positive and negative social (play faces/threat faces or nonsocial (banana/cage locks stimuli, plus food. Incorrect trials were not rewarded. Results demonstrated a strong effect of socially-reinforced learning, monkeys’ performed significantly better when reinforced with social versus nonsocial stimuli. Additionally, socially-reinforced learning was significantly better and occurred faster after IN-OT compared to placebo treatment. Performance in the IN-OT, but not Placebo, condition was also significantly better when the reinforcement stimuli were emotionally positive compared to negative facial expressions. These data support the hypothesis that OT may function to enhance prosocial behavior in primates by increasing the rewarding quality of emotionally positive, social compared to emotionally negative or nonsocial images. These data also support the use of the rhesus monkey as a model for exploring the neurobiological basis of social behavior and its impairment.

  19. An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

    Science.gov (United States)

    Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

    2015-08-13

    It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

  20. applying reinforcement learning to the weapon assignment problem

    African Journals Online (AJOL)

    ismith

    Carlo (MC) control algorithm with exploring starts (MCES), and an off-policy ..... closest to the threat should fire (that weapon also had the highest probability to ... Monte Carlo ..... “Reinforcement learning: Theory, methods and application to.

  1. Optical Generation of Fuzzy-Based Rules

    Science.gov (United States)

    Gur, Eran; Mendlovic, David; Zalevsky, Zeev

    2002-08-01

    In the last third of the 20th century, fuzzy logic has risen from a mathematical concept to an applicable approach in soft computing. Today, fuzzy logic is used in control systems for various applications, such as washing machines, train-brake systems, automobile automatic gear, and so forth. The approach of optical implementation of fuzzy inferencing was given by the authors in previous papers, giving an extra emphasis to applications with two dominant inputs. In this paper the authors introduce a real-time optical rule generator for the dual-input fuzzy-inference engine. The paper briefly goes over the dual-input optical implementation of fuzzy-logic inferencing. Then, the concept of constructing a set of rules from given data is discussed. Next, the authors show ways to implement this procedure optically. The discussion is accompanied by an example that illustrates the transformation from raw data into fuzzy set rules.

  2. IMPLEMENTATION OF FUZZY LOGIC BASED TEMPERATURE ...

    African Journals Online (AJOL)

    transfer function is derived based on process reaction curve obtained from a heat exchanger pilot plant ... The results show that the control performance for a Fuzzy controller is quite similar to ..... Process. Control Instrumentation Technology.

  3. Applying reinforcement learning to the weapon assignment problem in air defence

    CSIR Research Space (South Africa)

    Mouton, H

    2011-12-01

    Full Text Available . The techniques investigated in this article were two methods from the machine-learning subfield of reinforcement learning (RL), namely a Monte Carlo (MC) control algorithm with exploring starts (MCES), and an off-policy temporal-difference (TD) learning...

  4. The combination of appetitive and aversive reinforcers and the nature of their interaction during auditory learning.

    Science.gov (United States)

    Ilango, A; Wetzel, W; Scheich, H; Ohl, F W

    2010-03-31

    Learned changes in behavior can be elicited by either appetitive or aversive reinforcers. It is, however, not clear whether the two types of motivation, (approaching appetitive stimuli and avoiding aversive stimuli) drive learning in the same or different ways, nor is their interaction understood in situations where the two types are combined in a single experiment. To investigate this question we have developed a novel learning paradigm for Mongolian gerbils, which not only allows rewards and punishments to be presented in isolation or in combination with each other, but also can use these opposite reinforcers to drive the same learned behavior. Specifically, we studied learning of tone-conditioned hurdle crossing in a shuttle box driven by either an appetitive reinforcer (brain stimulation reward) or an aversive reinforcer (electrical footshock), or by a combination of both. Combination of the two reinforcers potentiated speed of acquisition, led to maximum possible performance, and delayed extinction as compared to either reinforcer alone. Additional experiments, using partial reinforcement protocols and experiments in which one of the reinforcers was omitted after the animals had been previously trained with the combination of both reinforcers, indicated that appetitive and aversive reinforcers operated together but acted in different ways: in this particular experimental context, punishment appeared to be more effective for initial acquisition and reward more effective to maintain a high level of conditioned responses (CRs). The results imply that learning mechanisms in problem solving were maximally effective when the initial punishment of mistakes was combined with the subsequent rewarding of correct performance. Copyright 2010 IBRO. Published by Elsevier Ltd. All rights reserved.

  5. Selection of Vendor Based on Intuitionistic Fuzzy Analytical Hierarchy Process

    Directory of Open Access Journals (Sweden)

    Prabjot Kaur

    2014-01-01

    Full Text Available Business environment is characterized by greater domestic and international competitive position in the global market. Vendors play a key role in achieving the so-called corporate competition. It is not easy however to identify good vendors because evaluation is based on multiple criteria. In practice, for VSP most of the input information about the criteria is not known precisely. Intuitionistic fuzzy set is an extension of the classical fuzzy set theory (FST, which is a suitable way to deal with impreciseness. In other words, the application of intuitionistic fuzzy sets instead of fuzzy sets means the introduction of another degree of freedom called nonmembership function into the set description. In this paper, we proposed a triangular intuitionistic fuzzy number based approach for the vendor selection problem using analytical hierarchy process. The crisp data of the vendors is represented in the form of triangular intuitionistic fuzzy numbers. By applying AHP which involves decomposition, pairwise comparison, and deriving priorities for the various levels of the hierarchy, an overall crisp priority is obtained for ranking the best vendor. A numerical example illustrates our method. Lastly a sensitivity analysis is performed to find the most critical criterion on the basis of which vendor is selected.

  6. Fuzzy logic based variable speed wind generation system

    Energy Technology Data Exchange (ETDEWEB)

    Simoes, M.G. [Sao Paulo Univ., SP (Brazil). Escola Politecnica. PMC - Mecatronica; Bose, B.K. [Tennessee Univ., Knoxville, TN (United States). Dept. of Electrical Engineering; Spiegel, Ronal J. [Environmental Protection Agency, Research Triangle Park, NC (United States). Air and Energy Engineering Research Lab.

    1996-12-31

    This work demonstrates the successful application of fuzzy logic to enhance the performance and control of a variable speed wind generation system. A maximum power point tracker control is performed with three fuzzy controllers, without wind velocity measurement, and robust to wind vortex and turbine torque ripple. A squirrel cage induction generator feeds the power to a double-sided PWM converter system which pumps the power to a utility grid or supplies to an autonomous system. The fuzzy logic controller FLC-1 searches on-line the generator speed so that the aerodynamic efficiency of the wind turbine is optimized. A second fuzzy controller FLC-2 programs the machine flux by on-line search so as to optimize the machine-converter system wind vortex. Detailed analysis and simulation studies were performed for development of the control strategy and fuzzy algorithms, and a DSP TMS320C30 based hardware with C control software was built for the performance evaluation of a laboratory experimental set-up. The theoretical development was fully validated and the system is ready to be reproduced in a higher power installation. (author) 7 refs., 3 figs., 1 tab.

  7. Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement

    Directory of Open Access Journals (Sweden)

    V. Magudeeswaran

    2013-01-01

    Full Text Available Fuzzy logic-based histogram equalization (FHE is proposed for image contrast enhancement. The FHE consists of two stages. First, fuzzy histogram is computed based on fuzzy set theory to handle the inexactness of gray level values in a better way compared to classical crisp histograms. In the second stage, the fuzzy histogram is divided into two subhistograms based on the median value of the original image and then equalizes them independently to preserve image brightness. The qualitative and quantitative analyses of proposed FHE algorithm are evaluated using two well-known parameters like average information contents (AIC and natural image quality evaluator (NIQE index for various images. From the qualitative and quantitative measures, it is interesting to see that this proposed method provides optimum results by giving better contrast enhancement and preserving the local information of the original image. Experimental result shows that the proposed method can effectively and significantly eliminate washed-out appearance and adverse artifacts induced by several existing methods. The proposed method has been tested using several images and gives better visual quality as compared to the conventional methods.

  8. The harmonics detection method based on neural network applied ...

    African Journals Online (AJOL)

    user

    Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total ..... Genetic algorithm-based self-learning fuzzy PI controller for shunt active filter, ... Verification of global optimality of the OFC active power filters by means of ...

  9. Machining and characterization of self-reinforced polymers

    Science.gov (United States)

    Deepa, A.; Padmanabhan, K.; Kuppan, P.

    2017-11-01

    This Paper focuses on obtaining the mechanical properties and the effect of the different machining techniques on self-reinforced composites sample and to derive the best machining method with remarkable properties. Each sample was tested by the Tensile and Flexural tests, fabricated using hot compaction test and those loads were calculated. These composites are machined using conventional methods because of lack of advanced machinery in most of the industries. The advanced non-conventional methods like Abrasive water jet machining were used. These machining techniques are used to get the better output for the composite materials with good mechanical properties compared to conventional methods. But the use of non-conventional methods causes the changes in the work piece, tool properties and more economical compared to the conventional methods. Finding out the best method ideal for the designing of these Self Reinforced Composites with and without defects and the use of Scanning Electron Microscope (SEM) analysis for the comparing the microstructure of the PP and PE samples concludes our process.

  10. Evaluation of B2C website based on the usability factors by using fuzzy AHP & hierarchical fuzzy TOPSIS

    Science.gov (United States)

    Masudin, I.; Saputro, T. E.

    2016-02-01

    In today's technology, electronic trading transaction via internet has been utilized properly with rapid growth. This paper intends to evaluate related to B2C e-commerce website in order to find out the one which meets the usability factors better than another. The influential factors to B2C e-commerce website are determined for two big retailer websites. The factors are investigated based on the consideration of several studies and conformed to the website characteristics. The evaluation is conducted by using different methods namely fuzzy AHP and hierarchical fuzzy TOPSIS so that the final evaluation can be compared. Fuzzy triangular number is adopted to deal with imprecise judgment under fuzzy environment.

  11. Technology and development of self-reinforced polymer composites

    NARCIS (Netherlands)

    Alcock, B.; Peijs, T.

    2013-01-01

    In recent years there has been an increasing amount of interest, both commercially and scientifically, in the emerging field of "self-reinforced polymer composites". These materials, which are sometimes also referred to as "single polymer composites", or "all-polymer composites", were first

  12. Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms

    CERN Document Server

    Siddique, Nazmul

    2014-01-01

    Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller.  The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...

  13. Self-tapping ability of carbon fibre reinforced polyetheretherketone suture anchors.

    Science.gov (United States)

    Feerick, Emer M; Wilson, Joanne; Jarman-Smith, Marcus; Ó'Brádaigh, Conchur M; McGarry, J Patrick

    2014-10-01

    An experimental and computational investigation of the self-tapping ability of carbon fibre reinforced polyetheretherketone (CFR-PEEK) has been conducted. Six CFR-PEEK suture anchor designs were investigated using PEEK-OPTIMA® Reinforced, a medical grade of CFR-PEEK. Experimental tests were conducted to investigate the maximum axial force and torque required for self-taping insertion of each anchor design. Additional experimental tests were conducted for some anchor designs using pilot holes. Computational simulations were conducted to determine the maximum stress in each anchor design at various stages of insertion. Simulations also were performed to investigate the effect of wall thickness in the anchor head. The maximum axial force required to insert a self-tapping CFR-PEEK suture anchor did not exceed 150 N for any anchor design. The maximum torque required to insert a self-tapping CFR-PEEK suture anchor did not exceed 0.8 Nm. Computational simulations reveal significant stress concentrations in the region of the anchor tip, demonstrating that a re-design of the tip geometry should be performed to avoid fracture during self-tapping, as observed in the experimental component of this study. This study demonstrates the ability of PEEK-OPTIMA Reinforced suture anchors to self-tap polyurethane foam bone analogue. This provides motivation to further investigate the self-tapping ability of CFR-PEEK suture anchors in animal/cadaveric bone. An optimised design for CFR-PEEK suture anchors offers the advantages of radiolucency, and mechanical properties similar to bone with the ability to self-tap. This may have positive implications for reducing surgery times and the associated costs with the procedure. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

  14. RLAM: A Dynamic and Efficient Reinforcement Learning-Based Adaptive Mapping Scheme in Mobile WiMAX Networks

    Directory of Open Access Journals (Sweden)

    M. Louta

    2014-01-01

    Full Text Available WiMAX (Worldwide Interoperability for Microwave Access constitutes a candidate networking technology towards the 4G vision realization. By adopting the Orthogonal Frequency Division Multiple Access (OFDMA technique, the latest IEEE 802.16x amendments manage to provide QoS-aware access services with full mobility support. A number of interesting scheduling and mapping schemes have been proposed in research literature. However, they neglect a considerable asset of the OFDMA-based wireless systems: the dynamic adjustment of the downlink-to-uplink width ratio. In order to fully exploit the supported mobile WiMAX features, we design, develop, and evaluate a rigorous adaptive model, which inherits its main aspects from the reinforcement learning field. The model proposed endeavours to efficiently determine the downlink-to-uplinkwidth ratio, on a frame-by-frame basis, taking into account both the downlink and uplink traffic in the Base Station (BS. Extensive evaluation results indicate that the model proposed succeeds in providing quite accurate estimations, keeping the average error rate below 15% with respect to the optimal sub-frame configurations. Additionally, it presents improved performance compared to other learning methods (e.g., learning automata and notable improvements compared to static schemes that maintain a fixed predefined ratio in terms of service ratio and resource utilization.

  15. Success Factors of Biotechnology Industry Based on Triangular Fuzzy Number

    OpenAIRE

    Lei, Lei

    2013-01-01

    Based on the theory of competitive advantage and value chain, this paper establishes the indicator system, and develop the strategic framework using the fuzzy Delphi method. Then the triangular fuzzy number model is established using Fuzzy Analytic Hierarchy Process, and the key factors influencing biotechnology industry are extracted. The results show that in terms of weight, the key factors influencing the success of biotechnology industry are sequenced as follows: “open innovation capaci...

  16. Fuzzy logic based ELF magnetic field estimation in substations

    International Nuclear Information System (INIS)

    Kosalay, I.

    2008-01-01

    This paper examines estimation of the extremely low frequency magnetic fields (MF) in the power substation. First, the results of the previous relevant research studies and the MF measurements in a sample power substation are presented. Then, a fuzzy logic model based on the geometric definitions in order to estimate the MF distribution is explained. Visual software, which has a three-dimensional screening unit, based on the fuzzy logic technique, has been developed. (authors)

  17. Fuzzy GML Modeling Based on Vague Soft Sets

    Directory of Open Access Journals (Sweden)

    Bo Wei

    2017-01-01

    Full Text Available The Open Geospatial Consortium (OGC Geography Markup Language (GML explicitly represents geographical spatial knowledge in text mode. All kinds of fuzzy problems will inevitably be encountered in spatial knowledge expression. Especially for those expressions in text mode, this fuzziness will be broader. Describing and representing fuzziness in GML seems necessary. Three kinds of fuzziness in GML can be found: element fuzziness, chain fuzziness, and attribute fuzziness. Both element fuzziness and chain fuzziness belong to the reflection of the fuzziness between GML elements and, then, the representation of chain fuzziness can be replaced by the representation of element fuzziness in GML. On the basis of vague soft set theory, two kinds of modeling, vague soft set GML Document Type Definition (DTD modeling and vague soft set GML schema modeling, are proposed for fuzzy modeling in GML DTD and GML schema, respectively. Five elements or pairs, associated with vague soft sets, are introduced. Then, the DTDs and the schemas of the five elements are correspondingly designed and presented according to their different chains and different fuzzy data types. While the introduction of the five elements or pairs is the basis of vague soft set GML modeling, the corresponding DTD and schema modifications are key for implementation of modeling. The establishment of vague soft set GML enables GML to represent fuzziness and solves the problem of lack of fuzzy information expression in GML.

  18. Assessing medical students' self-regulation as aptitude in computer-based learning.

    Science.gov (United States)

    Song, Hyuksoon S; Kalet, Adina L; Plass, Jan L

    2011-03-01

    We developed a Self-Regulation Measure for Computer-based learning (SRMC) tailored toward medical students, by modifying Zimmerman's Self-Regulated Learning Interview Schedule (SRLIS) for K-12 learners. The SRMC's reliability and validity were examined in 2 studies. In Study 1, 109 first-year medical students were asked to complete the SRMC. Bivariate correlation analysis results indicated that the SRMC scores had a moderate degree of correlation with student achievement in a teacher-developed test. In Study 2, 58 third-year clerkship students completed the SRMC. Regression analysis results indicated that the frequency of medical students' usage of self-regulation strategies was associated with their general clinical knowledge measured by a nationally standardized licensing exam. These two studies provided evidence for the reliability and concurrent validity of the SRMC to assess medical students' self-regulation as aptitude. Future work should provide evidence to guide and improve instructional design as well as inform educational policy.

  19. Reinforcement and Systemic Machine Learning for Decision Making

    CERN Document Server

    Kulkarni, Parag

    2012-01-01

    Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available-or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm-creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new an

  20. Switched Two-Level H∞ and Robust Fuzzy Learning Control of an Overhead Crane

    Directory of Open Access Journals (Sweden)

    Kao-Ting Hung

    2013-01-01

    Full Text Available Overhead cranes are typical dynamic systems which can be modeled as a combination of a nominal linear part and a highly nonlinear part. For such kind of systems, we propose a control scheme that deals with each part separately, yet ensures global Lyapunov stability. The former part is readily controllable by the H∞ PDC techniques, and the latter part is compensated by fuzzy mixture of affine constants, leaving the remaining unmodeled dynamics or modeling error under robust learning control using the Nelder-Mead simplex algorithm. Comparison with the adaptive fuzzy control method is given via simulation studies, and the validity of the proposed control scheme is demonstrated by experiments on a prototype crane system.