ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM
Institute of Scientific and Technical Information of China (English)
X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen
2003-01-01
An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.
Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.
Sobhani-Tehrani, E; Talebi, H A; Khorasani, K
2014-02-01
This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements.
Hybrid energy system evaluation in water supply system energy production: neural network approach
Energy Technology Data Exchange (ETDEWEB)
Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)
2010-07-01
Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.
Hybrid energy system evaluation in water supply system energy production: neural network approach
Directory of Open Access Journals (Sweden)
Fabio V. Goncalves, Helena M. Ramos, Luisa Fernanda R. Reis
2010-01-01
Full Text Available Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator – CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator – HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.
On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network
DEFF Research Database (Denmark)
Alizadeh, Tohid
2008-01-01
This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP......-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system....
Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System
Directory of Open Access Journals (Sweden)
ASTROV, I.
2007-04-01
Full Text Available This paper proposes a two-rate hybrid neural network system, which consists of two artificial neural network subsystems. These neural network subsystems are used as the dynamic subsystems controllers.1 This is because such neuromorphic controllers are especially suitable to control complex systems. An illustrative example - two-rate neural network hybrid control of decomposed stochastic model of a rigid guided missile over different operating conditions - was carried out using the proposed two-rate state-space decomposition technique. This example demonstrates that this research technique results in simplified low-order autonomous control subsystems with various speeds of actuation, and shows the quality of the proposed technique. The obtained results show that the control tasks for the autonomous subsystems can be solved more qualitatively than for the original system. The simulation and animation results with use of software package Simulink demonstrate that this research technique would work for real-time stochastic systems.
Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System
Directory of Open Access Journals (Sweden)
Y. D. Song
2013-01-01
Full Text Available This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.
Rezvani, Alireza; Khalili, Abbas; Mazareie, Alireza; Gandomkar, Majid
2016-07-01
Nowadays, photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is its dependence on weather conditions. Therefore, battery energy storage (BES) can be considered to assist for a stable and reliable output from PV generation system for loads and improve the dynamic performance of the whole generation system in grid connected mode. In this paper, a novel topology of intelligent hybrid generation systems with PV and BES in a DC-coupled structure is presented. Each photovoltaic cell has a specific point named maximum power point on its operational curve (i.e. current-voltage or power-voltage curve) in which it can generate maximum power. Irradiance and temperature changes affect these operational curves. Therefore, the nonlinear characteristic of maximum power point to environment has caused to development of different maximum power point tracking techniques. In order to capture the maximum power point (MPP), a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. Obtained results represent the effectiveness and superiority of the proposed method, and the average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison to the conventional methods. It has the advantages of robustness, fast response and good performance. A detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink.
Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.
Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao
2015-02-01
This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.
Energy Technology Data Exchange (ETDEWEB)
Kim, K.H. [Kangwon National Univ. (Korea, Republic of). Dept. of Electrical Engineering; Park, J.K. [Seoul National Univ. (Korea, Republic of). Dept. of Electrical Engineering; Hwang, K.J. [Univ. of Ulsan (Korea, Republic of). Dept. of Electrical Engineering; Kim, S.H. [Korea Electric Power Co., Seoul (Korea, Republic of). Power System Control Dept.
1995-08-01
In this paper, a hybrid model for short-term load forecast that integrates artificial neural networks and fuzzy expert systems is presented. The forecasted load is obtained by passing through two steps. In the first procedure, the artificial neural networks are trained with the load patterns corresponding to the forecasting hour, and the provisional forecasted load is obtained by the trained artificial neural networks. In the second procedure, the fuzzy expert systems modify the provisional forecasted load considering the possibility of load variation due to changes in temperature and the load behavior of holiday. In the test case of 1994 for implementation in short term load forecasting expert system of Korea Electric Power Corporation (KEPCO), the proposed hybrid model provided good forecasting accuracy of the mean absolute percentage errors below 1.3%. The comparison results with exponential smoothing method showed the efficiency and accuracy of the hybrid model.
Directory of Open Access Journals (Sweden)
G. Jiji
2014-04-01
Full Text Available Robot manipulator play important role in the field of automobile industry, mainly it is used in gas welding application and manufacturing and assembling of motor parts. In complex trajectory, on each joint the speed of the robot manipulator is affected. For that reason, it is necessary to analyze the noise and vibration of robot's joints for predicting faults also improve the control precision of robotic manipulator. In this study we will propose a new fault detection system for Robot manipulator. The proposed hybrid fault detection system is designed based on fuzzy support vector machine and Artificial Neural Networks (ANNs. In this system the decouple joints are identified and corrected using fuzzy SVM, here non-linear signal are used for complete process and treatment, the Artificial Neural Networks (ANNs are used to detect the free-swinging and locked joint of the robot, two types of neural predictors are also employed in the proposed adaptive neural network structure. The simulation results of a hybrid controller demonstrate the feasibility and performance of the methodology.
Using a hybrid neural/expert system for data base mining in market survey data
Energy Technology Data Exchange (ETDEWEB)
Ciesielski, V.; Palstra, G. [Royal Melbourne Inst. of Technology (Australia)
1996-12-31
This paper describes the application of a hybrid neural/expert system network to the task of finding significant events in a market research data base. Neural networks trained by backward error propagation are used to classify trends in the time series data. A rule system then uses these classifications, knowledge of market research analysis techniques and external events which influence the time series, to infer the significance of the data. The system achieved 86% recall and 100% precision on a test set of 6 months of survey data. This was significantly better than could be achieved by a system using linear regression together with a rule system. Both systems were able to perform analysis of the test data in under 5 minutes. The manual analysis of the same data took a human expert over four working days.
Hybrid information privacy system: integration of chaotic neural network and RSA coding
Hsu, Ming-Kai; Willey, Jeff; Lee, Ting N.; Szu, Harold H.
2005-03-01
Electronic mails are adopted worldwide; most are easily hacked by hackers. In this paper, we purposed a free, fast and convenient hybrid privacy system to protect email communication. The privacy system is implemented by combining private security RSA algorithm with specific chaos neural network encryption process. The receiver can decrypt received email as long as it can reproduce the specified chaos neural network series, so called spatial-temporal keys. The chaotic typing and initial seed value of chaos neural network series, encrypted by the RSA algorithm, can reproduce spatial-temporal keys. The encrypted chaotic typing and initial seed value are hidden in watermark mixed nonlinearly with message media, wrapped with convolution error correction codes for wireless 3rd generation cellular phones. The message media can be an arbitrary image. The pattern noise has to be considered during transmission and it could affect/change the spatial-temporal keys. Since any change/modification on chaotic typing or initial seed value of chaos neural network series is not acceptable, the RSA codec system must be robust and fault-tolerant via wireless channel. The robust and fault-tolerant properties of chaos neural networks (CNN) were proved by a field theory of Associative Memory by Szu in 1997. The 1-D chaos generating nodes from the logistic map having arbitrarily negative slope a = p/q generating the N-shaped sigmoid was given first by Szu in 1992. In this paper, we simulated the robust and fault-tolerance properties of CNN under additive noise and pattern noise. We also implement a private version of RSA coding and chaos encryption process on messages.
Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises.
Borrajo, M Lourdes; Baruque, Bruno; Corchado, Emilio; Bajo, Javier; Corchado, Juan M
2011-08-01
During the last years there has been a growing need of developing innovative tools that can help small to medium sized enterprises to predict business failure as well as financial crisis. In this study we present a novel hybrid intelligent system aimed at monitoring the modus operandi of the companies and predicting possible failures. This system is implemented by means of a neural-based multi-agent system that models the different actors of the companies as agents. The core of the multi-agent system is a type of agent that incorporates a case-based reasoning system and automates the business control process and failure prediction. The stages of the case-based reasoning system are implemented by means of web services: the retrieval stage uses an innovative weighted voting summarization of self-organizing maps ensembles-based method and the reuse stage is implemented by means of a radial basis function neural network. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.
A hybrid neural network system for prediction and recognition of promoter regions in human genome
Institute of Scientific and Technical Information of China (English)
CHEN Chuan-bo; LI Tao
2005-01-01
This paper proposes a high specificity and sensitivity algorithm called PromPredictor for recognizing promoter regions in the human genome. PromPredictor extracts compositional features and CpG islands information from genomic sequence,feeding these features as input for a hybrid neural network system (HNN) and then applies the HNN for prediction. It combines a novel promoter recognition model, coding theory, feature selection and dimensionality reduction with machine learning algorithm.Evaluation on Human chromosome 22 was ～66% in sensitivity and ～48% in specificity. Comparison with two other systems revealed that our method had superior sensitivity and specificity in predicting promoter regions. PromPredictor is written in MATLAB and requires Matlab to run. PromPredictor is freely available at http://www.whtelecom.com/Prompredictor.htm.
HYBRID OF FUZZY CLUSTERING NEURAL NETWORK OVER NSL DATASET FOR INTRUSION DETECTION SYSTEM
Directory of Open Access Journals (Sweden)
Dahlia Asyiqin Ahmad Zainaddin
2013-01-01
Full Text Available Intrusion Detection System (IDS is one of the component that take part in the system defence, to identify abnormal activities happening in the computer system. Nowadays, IDS facing composite demands to defeat modern attack activities from damaging the computer systems. Anomaly-Based IDS examines ongoing traffic, activity, transactions and behavior in order to identify intrusions by detecting anomalies. These technique identifies activities which degenerates from the normal behaviours. In recent years, data mining approach for intrusion detection have been advised and used. The approach such as Genetic Algorithms , Support Vector Machines, Neural Networks as well as clustering has resulted in high accuracy and good detection rates but with moderate false alarm on novel attacks. Many researchers also have proposed hybrid data mining techniques. The previous resechers has intoduced the combination of Fuzzy Clustering and Artificial Neural Network. However, it was tested only on randomn selection of KDDCup 1999 dataset. In this study the framework experiment introduced, has been used over the NSL dataset to test the stability and reliability of the technique. The result of precision, recall and f-value rate is compared with previous experiment. Both dataset covers four types of main attacks, which are Derial of Services (DoS, User to Root (U2R, Remote to Local (R2L and Probe. Results had guarenteed that the hybrid approach performed better detection especially for low frequent over NSL datataset compared to original KDD dataset, due to the removal of redundancy and uncomplete elements in the original dataset. This electronic document is a âliveâ template. The various components of your paper [title, text, tables, figures and references] are already defined on the style sheet, as illustrated by the portions given in this document.
2015-01-01
In recent past, it has been seen in many applications that synergism of computational intelligence techniques outperforms over an individual technique. This paper proposes a new hybrid computation model which is a novel synergism of modified evolutionary fuzzy clustering with associated neural networks. It consists of two modules: fuzzy distribution and neural classifier. In first module, mean patterns are distributed into the number of clusters based on the modified evolutionary fuzzy cluste...
Directory of Open Access Journals (Sweden)
Attariuas Hicham
2012-12-01
Full Text Available ales forecasting is one of the most crucial issues addressed in business. Control and evaluation of future sales still seem concerned both researchers and policy makers and managers of companies. this research propose an intelligent hybrid sales forecasting system Delphi-FCBPN sales forecast based on Delphi Method, fuzzy clustering and Back-propagation (BP Neural Networks with adaptive learning rate. The proposed model is constructed to integrate expert judgments, using Delphi method, in enhancing the model of FCBPN. Winter’s Exponential Smoothing method will be utilized to take the trend effect into consideration. The data for this search come from an industrial company that manufactures packaging. Analyze of results show that the proposed model outperforms other three different forecasting models in MAPE and RMSE measures.
Hybrid artificial neural network system for short-term load forecasting
Directory of Open Access Journals (Sweden)
Ilić Slobodan A.
2012-01-01
Full Text Available This paper presents a novel hybrid method for Short-Term Load Forecasting (STLF. The system comprises of two Artificial Neural Networks (ANN, assembled in a hierarchical order. The first ANN is a Multilayer Perceptron (MLP which functions as integrated load predictor (ILP for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor (HLP for a forecasting day. By using a separate ANN that predicts the integral of the load (ILP, additional information is presented to the actual forecasting ANN (HLP, while keeping its input space relatively small. This property enables online training and adaptation, as new data become available, because of the short training time. Different sizes of training sets have been tested, and the optimum of 30 day sliding time-window has been determined. The system has been verified on recorded data from Serbian electrical utility company. The results demonstrate better efficiency of the proposed method in comparison to non-hybrid methods because it produces better forecasts and yields smaller mean average percentage error (MAPE.
Directory of Open Access Journals (Sweden)
A. M. Chandrashekhar
2013-02-01
Full Text Available Intrusion Detection Systems (IDS form a key part of system defence, where it identifies abnormalactivities happening in a computer system. In recent years different soft computing based techniques havebeen proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfecttechnology. This has provided an opportunity for data mining to make quite a lot of importantcontributions in the field of intrusion detection. In this paper we have proposed a new hybrid techniqueby utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neurofuzzyand radial basis function(RBF SVM for fortification of the intrusion detection system. Theproposed technique has five major steps in which, first step is to perform the relevance analysis, and theninput data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that eachof the data point is trained with the corresponding neuro-fuzzy classifier associated with the cluster.Subsequently, a vector for SVM classification is formed and in the last step, classification using RBFSVMis performed to detect intrusion has happened or not. Data set used is the KDD cup 1999 datasetand we have used precision, recall, F-measure and accuracy as the evaluation metrics parameters. Ourtechnique could achieve better accuracy for all types of intrusions. The results of proposed technique arecompared with the other existing techniques. These comparisons proved the effectiveness of ourtechnique.
Hybrid intelligent engineering systems
Jain, L C; Adelaide, Australia University of
1997-01-01
This book on hybrid intelligent engineering systems is unique, in the sense that it presents the integration of expert systems, neural networks, fuzzy systems, genetic algorithms, and chaos engineering. It shows that these new techniques enhance the capabilities of one another. A number of hybrid systems for solving engineering problems are presented.
Weather forecasting based on hybrid neural model
Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.
2017-02-01
Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.
Directory of Open Access Journals (Sweden)
Ali A. Abed
2016-06-01
Full Text Available The reluctance of industry to allow wireless paths to be incorporated in process control loops has limited the potential applications and benefits of wireless systems. The challenge is to maintain the performance of a control loop, which is degraded by slow data rates and delays in a wireless path. To overcome these challenges, this paper presents an application–level design for a wireless sensor/actuator network (WSAN based on the “automated architecture”. The resulting WSAN system is used in the developing of a wireless distributed control system (WDCS. The implementation of our wireless system involves the building of a wireless sensor network (WSN for data acquisition and controller area network (CAN protocol fieldbus system for plant actuation. The sensor/actuator system is controlled by an intelligent digital control algorithm that involves a controller developed with velocity PID-like Fuzzy Neural Petri Net (FNPN system. This control system satisfies two important real-time requirements: bumpless transfer and anti-windup, which are needed when manual/auto operating aspect is adopted in the system. The intelligent controller is learned by a learning algorithm based on back-propagation. The concept of petri net is used in the development of FNN to get a correlation between the error at the input of the controller and the number of rules of the fuzzy-neural controller leading to a reduction in the number of active rules. The resultant controller is called robust fuzzy neural petri net (RFNPN controller which is created as a software model developed with MATLAB. The developed concepts were evaluated through simulations as well validated by real-time experiments that used a plant system with a water bath to satisfy a temperature control. The effect of disturbance is also studied to prove the system's robustness.
Energy Technology Data Exchange (ETDEWEB)
Ondrej Linda; Dumidu Wijayasekara; Milos Manic; Craig Rieger
2014-11-01
Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a Fuzzy-Neural Data Fusion Engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFE is composed of a three-layered system consisting of: 1) traditional threshold based alarms, 2) anomalous behavior detector using self-organizing fuzzy logic system, and 3) artificial neural network based system modeling and prediction. The improved control system state-awareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratory’s (INL) hybrid energy system facility know as HYTEST. Experimental results demonstrate that the proposed FN-DFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold based alarm systems.
Directory of Open Access Journals (Sweden)
Ch. Sanjay
2014-12-01
Full Text Available In machining processes, drilling operation is material removal process that has been widely used in manufacturing since industrial revolution. The useful life of cutting tool and its operating conditions largely controls the economics of machining operations. Drilling is most frequently performed material removing process and is used as a preliminary step for many operations, such as reaming, tapping, and boring. Drill wear has a bad effect on the surface finish and dimensional accuracy of the work piece. The surface finish of a machined part is one of the most important quality characteristics in manufacturing industries. The primary objective of this research is the prediction of suitable parameters for surface roughness in drilling. Cutting speed, cutting force, and machining time were given as inputs to the adaptive fuzzy neural network and neuro-fuzzy analysis for estimating the values of surface roughness by using 2, 3, 4, and 5 membership functions. The best structures were selected based on minimum of summation of square with the actual values with the estimated values by artificial neural fuzzy inference system (ANFIS and neuro-fuzzy systems. For artificial neural network (ANN analysis, the number of neurons was selected from 1, 2, 3, … , 20. The learning rate was selected as .5 and .5 smoothing factor was used. The inputs were selected as cutting speed, feed, machining time, and thrust force. The best structures of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. Drilling experiments with 10 mm size were performed at two cutting speeds and feeds. Comparative analysis has been done between the actual values and the estimated values obtained by ANFIS, neuro-fuzzy, and ANN analysis.
Giraud, Francois
1999-10-01
This dissertation investigates the application of neural network theory to the analysis of a 4-kW Utility-interactive Wind-Photovoltaic System (WPS) with battery storage. The hybrid system comprises a 2.5-kW photovoltaic generator and a 1.5-kW wind turbine. The wind power generator produces power at variable speed and variable frequency (VSVF). The wind energy is converted into dc power by a controlled, tree-phase, full-wave, bridge rectifier. The PV power is maximized by a Maximum Power Point Tracker (MPPT), a dc-to-dc chopper, switching at a frequency of 45 kHz. The whole dc power of both subsystems is stored in the battery bank or conditioned by a single-phase self-commutated inverter to be sold to the utility at a predetermined amount. First, the PV is modeled using Artificial Neural Network (ANN). To reduce model uncertainty, the open-circuit voltage VOC and the short-circuit current ISC of the PV are chosen as model input variables of the ANN. These input variables have the advantage of incorporating the effects of the quantifiable and non-quantifiable environmental variants affecting the PV power. Then, a simplified way to predict accurately the dynamic responses of the grid-linked WPS to gusty winds using a Recurrent Neural Network (RNN) is investigated. The RNN is a single-output feedforward backpropagation network with external feedback, which allows past responses to be fed back to the network input. In the third step, a Radial Basis Functions (RBF) Network is used to analyze the effects of clouds on the Utility-Interactive WPS. Using the irradiance as input signal, the network models the effects of random cloud movement on the output current, the output voltage, the output power of the PV system, as well as the electrical output variables of the grid-linked inverter. Fourthly, using RNN, the combined effects of a random cloud and a wind gusts on the system are analyzed. For short period intervals, the wind speed and the solar radiation are considered as
Harmon, Frederick G; Frank, Andrew A; Joshi, Sanjay S
2005-01-01
A Simulink model, a propulsion energy optimization algorithm, and a CMAC controller were developed for a small parallel hybrid-electric unmanned aerial vehicle (UAV). The hybrid-electric UAV is intended for military, homeland security, and disaster-monitoring missions involving intelligence, surveillance, and reconnaissance (ISR). The Simulink model is a forward-facing simulation program used to test different control strategies. The flexible energy optimization algorithm for the propulsion system allows relative importance to be assigned between the use of gasoline, electricity, and recharging. A cerebellar model arithmetic computer (CMAC) neural network approximates the energy optimization results and is used to control the parallel hybrid-electric propulsion system. The hybrid-electric UAV with the CMAC controller uses 67.3% less energy than a two-stroke gasoline-powered UAV during a 1-h ISR mission and 37.8% less energy during a longer 3-h ISR mission.
Directory of Open Access Journals (Sweden)
Yongcheng Li
Full Text Available Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.
Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi
2015-01-01
Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.
Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems
Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik
2016-12-01
Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.
HYBRID OF FUZZY CLUSTERING NEURAL NETWORK OVER NSL DATASET FOR INTRUSION DETECTION SYSTEM
2013-01-01
Intrusion Detection System (IDS) is one of the component that take part in the system defence, to identify abnormal activities happening in the computer system. Nowadays, IDS facing composite demands to defeat modern attack activities from damaging the computer systems. Anomaly-Based IDS examines ongoing traffic, activity, transactions and behavior in order to identify intrusions by detecting anomalies. These technique identifies activities which degenerates from the normal behaviours. In rec...
Hybrid neural network models of transducers
Xie, Shilin; Zhang, Xinong; Chen, Shenglai; Zhu, Changchun
2011-10-01
A hybrid neural network (NN) approach is proposed and applied to modeling of transducers in the paper. The modeling procedures are also presented in detail. First, the simulated studies on the modeling of single input-single output and multi input-multi output transducers are conducted respectively by use of the developed hybrid NN scheme. Secondly, the hybrid NN modeling approach is utilized to characterize a six-axis force sensor prototype based on the measured data. The results show that the hybrid NN approach can significantly improve modeling precision in comparison with the conventional modeling method. In addition, the method is superior to NN black-box modeling because the former possesses smaller network scale, higher convergence speed, higher model precision and better generalization performance.
Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...
Evolvable Neural Software System
Curtis, Steven A.
2009-01-01
The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.
Time Series Prediction based on Hybrid Neural Networks
Directory of Open Access Journals (Sweden)
S. A. Yarushev
2016-01-01
Full Text Available In this paper, we suggest to use hybrid approach to time series forecasting problem. In first part of paper, we create a literature review of time series forecasting methods based on hybrid neural networks and neuro-fuzzy approaches. Hybrid neural networks especially effective for specific types of applications such as forecasting or classification problem, in contrast to traditional monolithic neural networks. These classes of problems include problems with different characteristics in different modules. The main part of paper create a detailed overview of hybrid networks benefits, its architectures and performance under traditional neural networks. Hybrid neural networks models for time series forecasting are discussed in the paper. Experiments with modular neural networks are given.
Directory of Open Access Journals (Sweden)
V. Dvadnenko
2016-06-01
Full Text Available The hybrid vehicle control system includes a start–stop system for an internal combustion engine. The system works in a hybrid mode and normal vehicle operation. To simplify the start–stop system, there were user new possibilities of a hybrid car, which appeared after the conversion. Results of the circuit design of the proposed system of basic blocks are analyzed.
DEFF Research Database (Denmark)
Ronkko, Mauno; Ravn, Anders P.
1997-01-01
a differential action, which allows differential equations as primitive actions. The extension allows us to model hybrid systems with both continuous and discrete behaviour. The main result of this paper is an extension of such a hybrid action system with parallel composition. The extension does not change...... the original meaning of the parallel composition, and therefore also the ordinary action systems can be composed in parallel with the hybrid action systems....
Powder processing of hybrid titanium neural electrodes
Lopez, Jose Luis, Jr.
A preliminary investigation into the powder production of a novel hybrid titanium neural electrode for EEG is presented. The rheological behavior of titanium powder suspensions using sodium alginate as a dispersant are examined for optimal slip casting conditions. Electrodes were slip cast and sintered at 950°C for 1 hr, 1000°C for 1, 3, and 6 hrs, and 1050°C for 1 hr. Residual porosities from sintering are characterized using Archimedes' technique and image analysis. The pore network is gel impregnated by submerging the electrodes in electrically conductive gel and placing them in a chamber under vacuum. Gel evaporation of the impregnated electrodes is examined. Electrodes are characterized in the dry and gelled states using impedance spectrometry and compared to a standard silver- silver chloride electrode. Power spectral densities for the sensors in the dry and gelled state are also compared. Residual porosities for the sintered specimens were between 50.59% and 44.81%. Gel evaporation tests show most of the impregnated gel evaporating within 20 min of exposure to atmospheric conditions with prolonged evaporation times for electrodes with higher impregnated gel mass. Impedance measurements of the produced electrodes indicate the low impedance of the hybrid electrodes are due to the increased contact area of the porous electrode. Power spectral densities of the titanium electrode behave similar to a standard silver-silver chloride electrode. Tests suggest the powder processed hybrid titanium electrode's performance is better than current dry contact electrodes and comparable to standard gelled silver-silver chloride electrodes.
ABOUT HYBRID BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DISCRETE DELAYS
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
In this paper, hybrid bidirectional associative memory neural networks with discrete delays is considered. By ingeniously importing real parameters di > 0(i = 1,2,···,n) which can be adjusted, we establish some new sufficient conditions for the dynamical characteristics of hybrid bidirectional associative memory neural networks with discrete delays by the method of variation of parameters and some analysis techniques. Our results generalize and improve the related results in [10,11]. Our work is significant...
DEFF Research Database (Denmark)
Olderog, Ernst-Rüdiger; Ravn, Anders Peter
2007-01-01
An introduction to three papers in a special issue on Hybrid Systems. These paper were first presented at an IFIP WG 2.2 meeting in Skagen 2005.......An introduction to three papers in a special issue on Hybrid Systems. These paper were first presented at an IFIP WG 2.2 meeting in Skagen 2005....
Hybrid systems with constraints
Daafouz, Jamal; Sigalotti, Mario
2013-01-01
Control theory is the main subject of this title, in particular analysis and control design for hybrid dynamic systems.The notion of hybrid systems offers a strong theoretical and unified framework to cope with the modeling, analysis and control design of systems where both continuous and discrete dynamics interact. The theory of hybrid systems has been the subject of intensive research over the last decade and a large number of diverse and challenging problems have been investigated. Nevertheless, many important mathematical problems remain open.This book is dedicated mainly to
Hybrid Neural Network Architecture for On-Line Learning
Chen, Yuhua; Wang, Lei
2008-01-01
Approaches to machine intelligence based on brain models have stressed the use of neural networks for generalization. Here we propose the use of a hybrid neural network architecture that uses two kind of neural networks simultaneously: (i) a surface learning agent that quickly adapt to new modes of operation; and, (ii) a deep learning agent that is very accurate within a specific regime of operation. The two networks of the hybrid architecture perform complementary functions that improve the overall performance. The performance of the hybrid architecture has been compared with that of back-propagation perceptrons and the CC and FC networks for chaotic time-series prediction, the CATS benchmark test, and smooth function approximation. It has been shown that the hybrid architecture provides a superior performance based on the RMS error criterion.
Directory of Open Access Journals (Sweden)
Wassim M. Haddad
2001-01-01
Full Text Available In this paper we develop a unified dynamical systems framework for a general class of systems possessing left-continuous flows; that is, left-continuous dynamical systems. These systems are shown to generalize virtually all existing notions of dynamical systems and include hybrid, impulsive, and switching dynamical systems as special cases. Furthermore, we generalize dissipativity, passivity, and nonexpansivity theory to left-continuous dynamical systems. Specifically, the classical concepts of system storage functions and supply rates are extended to left-continuous dynamical systems providing a generalized hybrid system energy interpretation in terms of stored energy, dissipated energy over the continuous-time dynamics, and dissipated energy over the resetting events. Finally, the generalized dissipativity notions are used to develop general stability criteria for feedback interconnections of left-continuous dynamical systems. These results generalize the positivity and small gain theorems to the case of left-continuous, hybrid, and impulsive dynamical systems.
A hybrid neural network model for noisy data regression.
Lee, Eric W M; Lim, Chee Peng; Yuen, Richard K K; Lo, S M
2004-04-01
A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.
DEFF Research Database (Denmark)
Rönnkö, M.; Ravn, Anders Peter; Sere, K.
2003-01-01
In this paper we investigate the use of action systems with differential actions in the specifcation of hybrid systems. As the main contribution we generalize the definition of a differential action, allowing the use of arbitrary relations over model variables and their time-derivatives in modell......In this paper we investigate the use of action systems with differential actions in the specifcation of hybrid systems. As the main contribution we generalize the definition of a differential action, allowing the use of arbitrary relations over model variables and their time...... parallel composition. Moreover, as the strength of the action system formalism is the support for stepwise development by refinement, we investigate refinement involving a differential action. We show that, due to the predicate transformer semantics, standard action refinement techniques apply also...... to the differential action, thus, allowing stepwise development of hybrid systems Udgivelsesdato: JAN 1...
Hybrid Neural Network and Support Vector Machine Method for Optimization
Rai, Man Mohan (Inventor)
2007-01-01
System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.
Exponential synchronization of general chaotic delayed neural networks via hybrid feedback
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, and covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks (CNNs), bidirectional associative memory (BAM) networks, recurrent multilayer perceptrons (RMLPs). By virtue of LyapunovKrasovskii stability theory and linear matrix inequality (LMI) technique, some exponential synchronization criteria are derived.Using the drive-response concept, hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria. Finally, detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.
Energy Technology Data Exchange (ETDEWEB)
Cardelli, E.; Faba, A. [Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia (Italy); Laudani, A.; Lozito, G.M.; Riganti Fulginei, F.; Salvini, A. [Department of Engineering, Roma Tre University, Via V. Volterra 62, 00146 Rome (Italy)
2016-04-01
This paper presents a hybrid neural network approach to model magnetic hysteresis at macro-magnetic scale. That approach aims to be coupled together with numerical treatments of magnetic hysteresis such as FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other similar devices, allowing a complete computer simulation with acceptable run times. The proposed Hybrid Neural System consists of four inputs representing the magnetic induction and magnetic field components at each time step and it is trained by 2D and scalar measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the Hybrid Neural System returns the predicted value of the field H at the same time step. Within the Hybrid Neural System, a suitably trained neural network is used for predicting the hysteretic behavior of the material to be modeled. Validations with experimental tests and simulations for symmetric, non-symmetric and minor loops are presented.
Recent Advances on Hybrid Intelligent Systems
Melin, Patricia; Kacprzyk, Janusz
2013-01-01
This book presents recent advances on hybrid intelligent systems using soft computing techniques for intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain groups of papers around a similar subject. The first part consists of papers with the main theme of hybrid intelligent systems for control and robotics, which are basically state of the art papers that propose new models and concepts, which can be the basis for achieving intelligent control and mobile robotics. The second part contains papers with the main theme of hybrid intelligent systems for pattern recognition and time series prediction, which are basically papers using nature-inspired techniques, like evolutionary algo...
Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks
DEFF Research Database (Denmark)
Wan, Can; Song, Yonghua; Xu, Zhao
2016-01-01
probabilities of prediction errors provide an alternative yet effective solution. This article proposes a hybrid artificial neural network approach to generate prediction intervals of wind power. An extreme learning machine is applied to conduct point prediction of wind power and estimate model uncertainties...... via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using...... actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications....
A hybrid training method for neural energy estimation in calorimetry
Da Silva, P V M; Seixas, J
2001-01-01
A neural mapping is developed to improve the overall performance of Tilecal, which is the hadronic calorimeter of the ATLAS detector. Feeding the input nodes of a multilayer feedforward neural network with the energy values sampled by the calorimeter cells in beam tests, it is shown that the original energy scale of pion beams is reconstructed over a wide energy range and linearity is significantly improved. As it happens for classical methods, a compromise between nonlinearity correction and the optimization of the energy resolution of the detector has to be accomplished. A hybrid training method for the neural mapping is proposed to achieve this design goal. Using the backpropagation algorithm, the method intercalates an epoch of training steps, for which the neural mapping mainly focus on linearity correction, with another block of training steps, in which the original energy resolution obtained by linearly combining the calorimeter cells becomes the main target. (6 refs).
Directory of Open Access Journals (Sweden)
Abazar Solgi
2014-01-01
Full Text Available Doubtlessly the first step in a river management is the precipitation modeling over the related watershed. However, considering high-stochastic property of the process, many models are still being developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently artificial neural network (ANN as a nonlinear interextrapolator is extensively used by hydrologists for precipitation modeling as well as other fields of hydrology. In the present study, wavelet analysis combined with artificial neural network and finally was compared with adaptive neurofuzzy system to predict the precipitation in Verayneh station, Nahavand, Hamedan, Iran. For this purpose, the original time series using wavelet theory decomposed to multiple subtime series. Then, these subseries were applied as input data for artificial neural network, to predict daily precipitation, and compared with results of adaptive neurofuzzy system. The results showed that the combination of wavelet models and neural networks has a better performance than adaptive neurofuzzy system, and can be applied to predict both short- and long-term precipitations.
Hybrid pre training algorithm of Deep Neural Networks
Directory of Open Access Journals (Sweden)
Drokin I. S.
2016-01-01
Full Text Available This paper proposes a hybrid algorithm of pre training deep networks, using both marked and unmarked data. The algorithm combines and extends the ideas of Self-Taught learning and pre training of neural networks approaches on the one hand, as well as supervised learning and transfer learning on the other. Thus, the algorithm tries to integrate in itself the advantages of each approach. The article gives some examples of applying of the algorithm, as well as its comparison with the classical approach to pre training of neural networks. These examples show the effectiveness of the proposed algorithm.
Karioja, Pentti; Mäkinen, Jukka-Tapani; Keränen, Kimmo; Aikio, Janne; Alajoki, Teemu; Jaakola, Tuomo; Koponen, Matti; Keränen, Antti; Heikkinen, Mikko; Tuomikoski, Markus; Suhonen, Riikka; Hakalahti, Leena; Kopola, Pälvi; Hast, Jukka; Liedert, Ralf; Hiltunen, Jussi; Masuda, Noriyuki; Kemppainen, Antti; Rönkä, Kari; Korhonen, Raimo
2012-04-01
This paper presents research activities carried out at VTT Technical Research Centre of Finland in the field of hybrid integration of optics, electronics and mechanics. Main focus area in our research is the manufacturing of electronic modules and product structures with printed electronics, film-over-molding and polymer sheet lamination technologies and the goal is in the next generation of smart systems utilizing monolithic polymer packages. The combination of manufacturing technologies such as roll-to-roll -printing, injection molding and traditional component assembly is called Printed Hybrid Systems (PHS). Several demonstrator structures have been made, which show the potential of polymer packaging technology. One demonstrator example is a laminated structure with embedded LED chips. Element thickness is only 0.3mm and the flexible stack of foils can be bent in two directions after assembly process and was shaped curved using heat and pressure. The combination of printed flexible circuit boards and injection molding has also been demonstrated with several functional modules. The demonstrators illustrate the potential of origami electronics, which can be cut and folded to 3D shapes. It shows that several manufacturing process steps can be eliminated by Printed Hybrid Systems technology. The main benefits of this combination are small size, ruggedness and conformality. The devices are ideally suited for medical applications as the sensitive electronic components are well protected inside the plastic and the structures can be cleaned easily due to the fact that they have no joints or seams that can accumulate dirt or bacteria.
Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.
Pan, Yongping; Yu, Haoyong
2017-06-01
This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.
A hybrid neural network model for consciousness
Institute of Scientific and Technical Information of China (English)
蔺杰; 金小刚; 杨建刚
2004-01-01
A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers,physical mnemonic layer and abstract thinking layer,which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness:(1)the reception process whereby cerebral subsystems group distributed signals into coherent object patterns;(2)the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and(3)the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns' changes. Using this framework,various sorts of human actions can be explained,leading to a general approach for analyzing brain functions.
A hybrid neural network model for consciousness
Institute of Scientific and Technical Information of China (English)
蔺杰; 金小刚; 杨建刚
2004-01-01
A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers, physical mnemonic layer and abstract thinking layer, which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness: (l) the reception process whereby cerebral subsystems group distributed signals into coherent object patterns; (2) the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and (3) the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns' changes. Using this framework, various sorts of human actions can be explained, leading to a general approach for analyzing brain functions.
Electroencephalography epilepsy classifications using hybrid cuckoo search and neural network
Pratiwi, A. B.; Damayanti, A.; Miswanto
2017-07-01
Epilepsy is a condition that affects the brain and causes repeated seizures. This seizure is episodes that can vary and nearly undetectable to long periods of vigorous shaking or brain contractions. Epilepsy often can be confirmed with an electrocephalography (EEG). Neural Networks has been used in biomedic signal analysis, it has successfully classified the biomedic signal, such as EEG signal. In this paper, a hybrid cuckoo search and neural network are used to recognize EEG signal for epilepsy classifications. The weight of the multilayer perceptron is optimized by the cuckoo search algorithm based on its error. The aim of this methods is making the network faster to obtained the local or global optimal then the process of classification become more accurate. Based on the comparison results with the traditional multilayer perceptron, the hybrid cuckoo search and multilayer perceptron provides better performance in term of error convergence and accuracy. The purpose methods give MSE 0.001 and accuracy 90.0 %.
Artificial Neural Network Analysis System
2007-11-02
Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis
Directory of Open Access Journals (Sweden)
Juliana Yim
2009-06-01
Full Text Available This paper looks at the ability of a relatively new technique, hybrid ANN’s, to predict corporate distress in Brazil. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting firms in financial distress one year prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid networks may be a useful tool for predicting firm failure.
Directory of Open Access Journals (Sweden)
Juliana Yim
2005-01-01
Full Text Available This paper looks at the ability of a relatively new technique, hybrid ANN's, to predict corporate distress in Brazil. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting firms in financial distress one year prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid networks may be a useful tool for predicting firm failure.
Hybridity in Embedded Computing Systems
Institute of Scientific and Technical Information of China (English)
虞慧群; 孙永强
1996-01-01
An embedded system is a system that computer is used as a component in a larger device.In this paper,we study hybridity in embedded systems and present an interval based temporal logic to express and reason about hybrid properties of such kind of systems.
Mantovanelli, Ivana C. C.; Rivera, Elmer Ccopa; da Costa, Aline C.; Filho, Rubens Maciel
In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These networks have been shown to have a good nonlinear approximation capability, although the estimation of its weights is linear. The proposed model considers the effect of temperature on the kinetics and has the neural network weights reestimated always so that a change in operational conditions occurs. This allow to follow the system behavior when changes in operating conditions occur.
The design, fabrication, and test of a new VLSI hybrid analog-digital neural processing element
Deyong, Mark R.; Findley, Randall L.; Fields, Chris
1992-01-01
A hybrid analog-digital neural processing element with the time-dependent behavior of biological neurons has been developed. The hybrid processing element is designed for VLSI implementation and offers the best attributes of both analog and digital computation. Custom VLSI layout reduces the layout area of the processing element, which in turn increases the expected network density. The hybrid processing element operates at the nanosecond time scale, which enables it to produce real-time solutions to complex spatiotemporal problems found in high-speed signal processing applications. VLSI prototype chips have been designed, fabricated, and tested with encouraging results. Systems utilizing the time-dependent behavior of the hybrid processing element have been simulated and are currently in the fabrication process. Future applications are also discussed.
The design, fabrication, and test of a new VLSI hybrid analog-digital neural processing element
Deyong, Mark R.; Findley, Randall L.; Fields, Chris
1992-01-01
A hybrid analog-digital neural processing element with the time-dependent behavior of biological neurons has been developed. The hybrid processing element is designed for VLSI implementation and offers the best attributes of both analog and digital computation. Custom VLSI layout reduces the layout area of the processing element, which in turn increases the expected network density. The hybrid processing element operates at the nanosecond time scale, which enables it to produce real-time solutions to complex spatiotemporal problems found in high-speed signal processing applications. VLSI prototype chips have been designed, fabricated, and tested with encouraging results. Systems utilizing the time-dependent behavior of the hybrid processing element have been simulated and are currently in the fabrication process. Future applications are also discussed.
Hybrid multiobjective evolutionary design for artificial neural networks.
Goh, Chi-Keong; Teoh, Eu-Jin; Tan, Kay Chen
2008-09-01
Evolutionary algorithms are a class of stochastic search methods that attempts to emulate the biological process of evolution, incorporating concepts of selection, reproduction, and mutation. In recent years, there has been an increase in the use of evolutionary approaches in the training of artificial neural networks (ANNs). While evolutionary techniques for neural networks have shown to provide superior performance over conventional training approaches, the simultaneous optimization of network performance and architecture will almost always result in a slow training process due to the added algorithmic complexity. In this paper, we present a geometrical measure based on the singular value decomposition (SVD) to estimate the necessary number of neurons to be used in training a single-hidden-layer feedforward neural network (SLFN). In addition, we develop a new hybrid multiobjective evolutionary approach that includes the features of a variable length representation that allow for easy adaptation of neural networks structures, an architectural recombination procedure based on the geometrical measure that adapts the number of necessary hidden neurons and facilitates the exchange of neuronal information between candidate designs, and a microhybrid genetic algorithm ( microHGA) with an adaptive local search intensity scheme for local fine-tuning. In addition, the performances of well-known algorithms as well as the effectiveness and contributions of the proposed approach are analyzed and validated through a variety of data set types.
Hybrid radiator cooling system
France, David M.; Smith, David S.; Yu, Wenhua; Routbort, Jules L.
2016-03-15
A method and hybrid radiator-cooling apparatus for implementing enhanced radiator-cooling are provided. The hybrid radiator-cooling apparatus includes an air-side finned surface for air cooling; an elongated vertically extending surface extending outwardly from the air-side finned surface on a downstream air-side of the hybrid radiator; and a water supply for selectively providing evaporative cooling with water flow by gravity on the elongated vertically extending surface.
Chaotic Dynamics in Hybrid Systems
P.J. Collins (Pieter)
2008-01-01
htmlabstractIn this paper we give an overview of some aspects of chaotic dynamics in hybrid systems, which comprise different types of behaviour. Hybrid systems may exhibit discontinuous dependence on initial conditions leading to new dynamical phenomena. We indicate how methods from topological
Chaotic dynamics in hybrid systems
P.J. Collins (Pieter)
2008-01-01
htmlabstractIn this paper we give an overview of some aspects of chaotic dynamics in hybrid systems, which comprise different types of behaviour. Hybrid systems may exhibit discontinuous dependence on initial conditions leading to new dynamical phenomena. We indicate how methods from topological
Hybrid spread spectrum radio system
Smith, Stephen F [London, TN; Dress, William B [Camas, WA
2010-02-09
Systems and methods are described for hybrid spread spectrum radio systems. A method, includes receiving a hybrid spread spectrum signal including: fast frequency hopping demodulating and direct sequence demodulating a direct sequence spread spectrum signal, wherein multiple frequency hops occur within a single data-bit time and each bit is represented by chip transmissions at multiple frequencies.
A Hybrid Artificial Neural Network-based Scheduling Knowledge Acquisition Algorithm
Institute of Scientific and Technical Information of China (English)
WANG Weida; WANG Wei; LIU Wenjian
2006-01-01
It is a key issue that constructing successful knowledge base to satisfy an efficient adaptive scheduling for the complex manufacturing system. Therefore, a hybrid artificial neural network (ANN)-based scheduling knowledge acquisition algorithm is presented in this paper. We combined genetic algorithm (GA) with simulated annealing (SA) to develop a hybrid optimization method, in which GA was introduced to present parallel search architecture and SA was introduced to increase escaping probability from local optima and ability to neighbor search. The hybrid method was utilized to resolve the optimal attributes subset of manufacturing system and determine the optimal topology and parameters of ANN under different scheduling objectives; ANN was used to evaluate the fitness of chromosome in the method and generate the scheduling knowledge after obtaining the optimal attributes subset, optimal ANN's topology and parameters. The experimental results demonstrate that the proposed algorithm produces significant performance improvements over other machine learning-based algorithms.
Memory Storage and Neural Systems.
Alkon, Daniel L.
1989-01-01
Investigates memory storage and molecular nature of associative-memory formation by analyzing Pavlovian conditioning in marine snails and rabbits. Presented is the design of a computer-based memory system (neural networks) using the rules acquired in the investigation. Reports that the artificial network recognized patterns well. (YP)
Using fuzzy logic to integrate neural networks and knowledge-based systems
Yen, John
1991-01-01
Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.
Towards Modelling of Hybrid Systems
DEFF Research Database (Denmark)
Wisniewski, Rafal
2006-01-01
The article is an attempt to use methods of category theory and topology for analysis of hybrid systems. We use the notion of a directed topological space; it is a topological space together with a set of privileged paths. Dynamical systems are examples of directed topological spaces. A hybrid...... system consists of a number of dynamical systems that are glued together according to information encoded in the discrete part of the system. We develop a definition of a hybrid system as a functor from the category generated by a transition system to the category of directed topological spaces. Its...... directed homotopy colimit (geometric realization) is a single directed topological space. The behavior of hybrid systems can be then understood in terms of the behavior of dynamical systems through the directed homotopy colimit....
A novel hybrid-maximum neural network in stereo-matching process.
Laskowski, Lukasz
2013-01-01
In the present paper, the completely innovative architecture of artificial neural network based on Hopfield structure for solving a stereo-matching problem-hybrid neural network, consisting of the classical analog Hopfield neural network and the Maximum Neural Network-is described. The application of this kind of structure as a part of assistive device for visually impaired individuals is considered. The role of the analog Hopfield network is to find the attraction area of the global minimum, whereas Maximum Neural Network is finding accurate location of this minimum. The network presented here is characterized by an extremely high rate of work performance with the same accuracy as a classical Hopfield-like network, which makes it possible to use this kind of structure as a part of systems working in real time. The network considered here underwent experimental tests with the use of real stereo pictures as well as simulated stereo images. This enables error calculation and direct comparison with the classic analog Hopfield neural network as well as other networks proposed in the literature.
Application of Hybrid Neural Network in Intrusion Detection System%混合神经网络在入侵检测系统中的应用研究
Institute of Scientific and Technical Information of China (English)
常磊; 宋玲; 吴丹
2012-01-01
提出一种基于MLP和Elman混合神经网络模型的入侵检测系统,旨在利用混合神经网络解决入侵检测问题。本模型具有记忆功能,可以有效地检测离散而又相联系的攻击行为。MLP网络是一个实时的模式分类器,而Elman网络实现了对事件的记忆能力。基于此混合模型的入侵检测系统使用DARPA数据集进行测试评估。实验证明基于此混合模型的入侵检测系统能够有效地提高检测率,降低误报率。%This paper proposes an intrusion detection system based on MLP and Elman hybrid neural network model which can be used to solve intrusion detection problems. This mode/ has the function of memory and can effectively detect discrete and related attacks. The MLP network is a real-time mode classifier, and the Elman network can realize the memorial function of an event. The intrusion detection system based on this hybrid model uses DARPA data set to test and estimate. Experiments prove that this intrusion detection system based on the above hybrid model can effectively improve detection rate and reduce false alarm rate.
Formal Description of Hybrid Systems
DEFF Research Database (Denmark)
Zhou, Chaochen; Ji, Wang; Ravn, Anders P.
1996-01-01
A language to describe hybrid systems, i.e. networks of communicating discrete and continuous processes, is proposed. A semantics of the language is given in Extended Duration Calculus, a real-time interval logic with a proof system that allows reasoning in mathematical analysis about continuous ...... processes to be embedded into the logic. The semantics thus provides a secure link to hybrid system models based on a general theory of dynamical systems....
Hybrid spacecraft attitude control system
Directory of Open Access Journals (Sweden)
Renuganth Varatharajoo
2016-02-01
Full Text Available The hybrid subsystem design could be an attractive approach for futurespacecraft to cope with their demands. The idea of combining theconventional Attitude Control System and the Electrical Power System ispresented in this article. The Combined Energy and Attitude ControlSystem (CEACS consisting of a double counter rotating flywheel assemblyis investigated for small satellites in this article. Another hybrid systemincorporating the conventional Attitude Control System into the ThermalControl System forming the Combined Attitude and Thermal ControlSystem (CATCS consisting of a "fluid wheel" and permanent magnets isalso investigated for small satellites herein. The governing equationsdescribing both these novel hybrid subsystems are presented and theironboard architectures are numerically tested. Both the investigated novelhybrid spacecraft subsystems comply with the reference missionrequirements.The hybrid subsystem design could be an attractive approach for futurespacecraft to cope with their demands. The idea of combining theconventional Attitude Control System and the Electrical Power System ispresented in this article. The Combined Energy and Attitude ControlSystem (CEACS consisting of a double counter rotating flywheel assemblyis investigated for small satellites in this article. Another hybrid systemincorporating the conventional Attitude Control System into the ThermalControl System forming the Combined Attitude and Thermal ControlSystem (CATCS consisting of a "fluid wheel" and permanent magnets isalso investigated for small satellites herein. The governing equationsdescribing both these novel hybrid subsystems are presented and theironboard architectures are numerically tested. Both the investigated novelhybrid spacecraft subsystems comply with the reference missionrequirements.
DIAGNOSIS WINDOWS PROBLEMS BASED ON HYBRID INTELLIGENCE SYSTEMS
Directory of Open Access Journals (Sweden)
SAFWAN O. HASOON
2013-10-01
Full Text Available This paper describes the artificial intelligence technologies by integrating Radial Basis Function networks with expert systems to construct a robust hybrid system. The purpose of building the hybrid system is to give recommendations to repair the operating system (Windows problems and troubleshoot the problems that can be repaired. The neural network has unique characteristics which it can complete the uncompleted data, the expert system can't deal with data that is incomplete, but using the neural network individually has some disadvantages which it can't give explanations and recommendations to the problems. The expert system has the ability to explain and give recommendations by using the rules and the human expert in some conditions. Therefore, we have combined the two technologies. The paper will explain the integration methods between the two technologies and which method is suitable to be used in the proposed hybrid system.
Institute of Scientific and Technical Information of China (English)
卢芸; 赵永来
2014-01-01
The use of wind power energy storage system is an effective measure to stabilize fluctuations in wind power today, however, the energy storage system control strategy has a direct impact on the wind power system's technical and economic performance. Based on the complementary in function of super-capacitor and battery, its application in wind farms based DFIG and the topology control structures for wind farm with distributed rectifier and centralized inverter are adopted, fuzzy neural PID controller is designed, and fuzzy neural network algorithm is used to optimize PID control parameters for hybrid energy storage system on line. Based on MATLAB / SIMULINK platform, the control system simulation model is established and analyzed. Simulation results show that the hybrid energy storage system can improve the life of the energy storage device. According to the compensate power and the fluctuation range of the state of charge of the energy storage systems, as well as wind power fluctuations in the degree of smoothing, the effectiveness of the control system is verified.%采用风电储能系统来平抑风电波动功率在当今是一个有效的措施，然而储能系统控制策略的好坏直接影响风电系统的技术性能和经济性能。根据超级电容器和蓄电池在功能上的互补性，将其应用在基于双馈电机的风电场中，风电场采用分布整流集中逆变拓扑控制结构，并对其设计模糊神经PID控制器，采用模糊神经网络算法对混合储能系统PID控制参数进行在线优化。基于Matlab/Simulink平台搭建控制系统仿真模型，并进行仿真分析，验证了混合储能系统能够提高储能装置的使用寿命。根据储能系统补偿功率和其荷电状态的波动范围，以及对风电波动功率的平滑程度，验证了该控制系统的有效性。
Energy Technology Data Exchange (ETDEWEB)
Meier, E.; Morgan, M. J.; Biedron, S. G.; LeBlanc, G.; Wu, J. (OTD-ESE); (Monash Univ.); (Australian Synchrotron Project); (SLAC National Accelerator Lab.)
2009-01-01
This paper describes the implementation of a neural network hybrid controller for energy stabilization at the Australian Synchrotron Linac. The structure of the controller consists of a neural network (NNET) feed forward control, augmented by a conventional Proportional-Integral (PI) feedback controller to ensure stability of the system. The system is provided with past states of the machine in order to predict its future state, and therefore apply appropriate feed forward control. The NNET is able to cancel multiple frequency jitter in real-time. When it is not performing optimally due to jitter changes, the system can successfully be augmented by the PI controller to attenuate the remaining perturbations. With a view to control the energy and bunch length at the FERMI{at}Elettra Free Electron Laser (FEL), the present study considers a neural network hybrid feed forward-feedback type of control to rectify limitations related to feedback systems, such as poor response for high jitter frequencies or limited bandwidth, while ensuring robustness of control. The Australian Synchrotron Linac is equipped with a beam position monitor (BPM), that was provided by Sincrotrone Trieste from a former transport line thus allowing energy measurements and energy control experiments. The present study will consequently focus on correcting energy jitter induced by variations in klystron phase and voltage.
Hybrid Algorithm for the Optimization of Training Convolutional Neural Network
Directory of Open Access Journals (Sweden)
Hayder M. Albeahdili
2015-10-01
Full Text Available The training optimization processes and efficient fast classification are vital elements in the development of a convolution neural network (CNN. Although stochastic gradient descend (SGD is a Prevalence algorithm used by many researchers for the optimization of training CNNs, it has vast limitations. In this paper, it is endeavor to diminish and tackle drawbacks inherited from SGD by proposing an alternate algorithm for CNN training optimization. A hybrid of genetic algorithm (GA and particle swarm optimization (PSO is deployed in this work. In addition to SGD, PSO and genetic algorithm (PSO-GA are also incorporated as a combined and efficient mechanism in achieving non trivial solutions. The proposed unified method achieves state-of-the-art classification results on the different challenge benchmark datasets such as MNIST, CIFAR-10, and SVHN. Experimental results showed that the results outperform and achieve superior results to most contemporary approaches.
Hybrid-Vehicle Transmission System
Lupo, G.; Dotti, G.
1985-01-01
Continuously-variable transmission system for hybrid vehicles couples internal-combustion engine and electric motor section, either individually or in parallel, to power vehicle wheels during steering and braking.
Hybrid-Vehicle Transmission System
Lupo, G.; Dotti, G.
1985-01-01
Continuously-variable transmission system for hybrid vehicles couples internal-combustion engine and electric motor section, either individually or in parallel, to power vehicle wheels during steering and braking.
Hybrid solar lighting distribution systems and components
Muhs, Jeffrey D.; Earl, Dennis D.; Beshears, David L.; Maxey, Lonnie C.; Jordan, John K.; Lind, Randall F.
2011-07-05
A hybrid solar lighting distribution system and components having at least one hybrid solar concentrator, at least one fiber receiver, at least one hybrid luminaire, and a light distribution system operably connected to each hybrid solar concentrator and each hybrid luminaire. A controller operates all components.
Hybrid solar lighting systems and components
Muhs, Jeffrey D.; Earl, Dennis D.; Beshears, David L.; Maxey, Lonnie C.; Jordan, John K.; Lind, Randall F.
2007-06-12
A hybrid solar lighting system and components having at least one hybrid solar concentrator, at least one fiber receiver, at least one hybrid luminaire, and a light distribution system operably connected to each hybrid solar concentrator and each hybrid luminaire. A controller operates each component.
Yamamoto, Takeyoshi; Cingoski, Vlatko; Kaneda, Kazufumi; Yamashita, Hideo
1996-01-01
In this paper, a hybrid method for inverse optimization of electromagnetic coils utilizing the multi-transition neural network and the Hopfield neural network is proposed. Due to the discrete character of the neural network, an optimization problem is transformed into a discrete problem through the division of the entire coil area into elemental coils with constant current density. The minimization of the objective function is performed by the multi-transition neural network and the Hopfield ...
ANOMALY INTRUSION DETECTION DESIGN USING HYBRID OF UNSUPERVISED AND SUPERVISED NEURAL NETWORK
Directory of Open Access Journals (Sweden)
M. Bahrololum
2009-07-01
Full Text Available This paper proposed a new approach to design the system using a hybrid of misuse and anomalydetection for training of normal and attack packets respectively. The utilized method for attack training isthe combination of unsupervised and supervised Neural Network (NN for Intrusion Detection System. Bythe unsupervised NN based on Self Organizing Map (SOM, attacks will be classified into smallercategories considering their similar features, and then unsupervised NN based on Backpropagation willbe used for clustering. By misuse approach known packets would be identified fast and unknown attackswill be able to detect by this method.
Model Reduction of Hybrid Systems
DEFF Research Database (Denmark)
Shaker, Hamid Reza
systems are derived in this thesis. The results are used for output feedback control of switched nonlinear systems. Model reduction of piecewise affine systems is also studied in this thesis. The proposed method is based on the reduction of linear subsystems inside the polytopes. The methods which......High-Technological solutions of today are characterized by complex dynamical models. A lot of these models have inherent hybrid/switching structure. Hybrid/switched systems are powerful models for distributed embedded systems design where discrete controls are applied to continuous processes...... of hybrid systems, designing controllers and implementations is very high so that the use of these models is limited in applications where the size of the state space is large. To cope with complexity, model reduction is a powerful technique. This thesis presents methods for model reduction and stability...
A hybrid base isolation system
Energy Technology Data Exchange (ETDEWEB)
Hart, G.C. [Univ. of California, Los Angeles, CA (United States); Lobo, R.F.; Srinivasan, M. [Hart Consultant Group, Santa Monica, CA (United States); Asher, J.W. [kpff Engineers, Santa Monica, CA (United States)
1995-12-01
This paper proposes a new analysis procedure for hybrid base isolation buildings when considering the displacement response of a base isolated building to wind loads. The system is considered hybrid because of the presence of viscous dampers in the building above the isolator level. The proposed analysis approach incorporates a detailed site specific wind study combined with a dynamic nonlinear analysis of the building response.
Willitsch, Stefan
2014-01-01
The study of interactions between simultaneously trapped cold ions and atoms has emerged as a new research direction in recent years. The development of ion-atom hybrid experiments has paved the way for investigating elastic, inelastic and reactive collisions between these species at very low temperatures, for exploring new cooling mechanisms of ions by atoms and for implementing new hybrid quantum systems. The present lecture reviews experimental methods, recent results and upcoming developments in this emerging field.
The LILARTI neural network system
Energy Technology Data Exchange (ETDEWEB)
Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.
1992-10-01
The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.
Gong, Dawei; Lewis, Frank L; Wang, Liping; Xu, Ke
2016-05-01
In this paper, a novel pinning synchronization (synchronization with pinning control) scheme for an array of neural networks with hybrid coupling is investigated. The main contributions are as follows: (1) A novel pinning control strategy is proposed for the first time. Pinning control schemes are introduced as an array of column vector. The controllers are designed as simple linear systems, which are easy to be analyzed or tested. (2) Augmented Lyapunov-Krasovskii functional (LKF) is applied to introduce more relax variables, which can alleviate the requirements of the positive definiteness of the matrix. (3) Based on the appropriate LKF, by introducing some free weighting matrices, some novel synchronization criteria are derived. Furthermore, the proposed pinning control scheme described by column vector can also be expanded to almost all the other array of neural networks. Finally, numerical examples are provided to show the effectiveness of the proposed results.
NEURAL NETWORKS CONTROL OF THE HYBRID POWER UNIT BASED ON THE METHOD OF ADAPTIVE CRITICS
Directory of Open Access Journals (Sweden)
S. Serikov
2012-01-01
Full Text Available The formal statement of the optimization problem of hybrid vehicle power unit control is given. Its solving by neural networks method application on the basis of adaptive critic is considered.
Seafloor classification using echo- waveforms: A method employing hybrid neural network architecture
Digital Repository Service at National Institute of Oceanography (India)
Chakraborty, B.; Mahale, V.; DeSouza, C.; Das, P.
This letter presents seafloor classification study results of a hybrid artificial neural network architecture known as learning vector quantization. Single beam echo-sounding backscatter waveform data from three different seafloors of the western...
Energy Technology Data Exchange (ETDEWEB)
Leupold, M.J.; Iwasa, Y.; Weggel, R.J. (MIT Cambridge (U.S.A.))
1984-01-01
The paper describes the design and construction of a hybrid magnet system to generate 32T with 9MW of electrical power. The system consist of an 11T niobium-titanium superconducting magnet, a 1.8K/4.2K cryostat, and a high-performance, water-cooled Bitter magnet, all of which are discussed in the paper.
Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control.
Kocaturk, Mehmet; Gulcur, Halil Ozcan; Canbeyli, Resit
2015-01-01
In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain-machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.
Hybrid Systems: Computation and Control.
2007-11-02
elbow) and a pinned first joint (shoul- der) (see Figure 2); it is termed an underactuated system since it is a mechanical system with fewer...Montreal, PQ, Canada, 1998. [10] M. W. Spong. Partial feedback linearization of underactuated mechanical systems . In Proceedings, IROS, pages 314-321...control mechanism and search for optimal combinations of control variables. Besides the nonlinear and hybrid nature of powertrain systems , hardware
Advanced Hybrid Computer Systems. Software Technology.
This software technology final report evaluates advances made in Advanced Hybrid Computer System software technology . The report describes what...automatic patching software is available as well as which analog/hybrid programming languages would be most feasible for the Advanced Hybrid Computer...compiler software . The problem of how software would interface with the hybrid system is also presented.
Lukas Falat; Dusan Marcek; Maria Durisova
2016-01-01
This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the sug...
Hybrid Power Management System and Method
Eichenberg, Dennis J. (Inventor)
2008-01-01
A system and method for hybrid power management. The system includes photovoltaic cells, ultracapacitors, and pulse generators. In one embodiment, the hybrid power management system is used to provide power for a highway safety flasher.
Hybrid intelligent monironing systems for thermal power plant trips
Barsoum, Nader; Ismail, Firas Basim
2012-11-01
Steam boiler is one of the main equipment in thermal power plants. If the steam boiler trips it may lead to entire shutdown of the plant, which is economically burdensome. Early boiler trips monitoring is crucial to maintain normal and safe operational conditions. In the present work two artificial intelligent monitoring systems specialized in boiler trips have been proposed and coded within the MATLAB environment. The training and validation of the two systems has been performed using real operational data captured from the plant control system of selected power plant. An integrated plant data preparation framework for seven boiler trips with related operational variables has been proposed for IMSs data analysis. The first IMS represents the use of pure Artificial Neural Network system for boiler trip detection. All seven boiler trips under consideration have been detected by IMSs before or at the same time of the plant control system. The second IMS represents the use of Genetic Algorithms and Artificial Neural Networks as a hybrid intelligent system. A slightly lower root mean square error was observed in the second system which reveals that the hybrid intelligent system performed better than the pure neural network system. Also, the optimal selection of the most influencing variables performed successfully by the hybrid intelligent system.
Dynamical systems revisited : Hybrid systems with Zeno executions
ZHANG, JUN; Johansson, Karl Henrik; Lygeros, John; Sastry, Shankar
2000-01-01
Results from classical dynamical systems are generalized to hybrid dynamical systems. The concept of omega limit set is introduced for hybrid systems and is used to prove new results on invariant sets and stability, where Zeno and non-Zeno hybrid systems can be treated within the same framework. As an example, LaSalle's Invariance Principle is extended to hybrid systems. Zeno hybrid systems are discussed in detail. The omega limit set of a Zeno execution is characterized for classes of hybrid...
Stochastic resonance enhancement of small-world neural networks by hybrid synapses and time delay
Yu, Haitao; Guo, Xinmeng; Wang, Jiang
2017-01-01
The synergistic effect of hybrid electrical-chemical synapses and information transmission delay on the stochastic response behavior in small-world neuronal networks is investigated. Numerical results show that, the stochastic response behavior can be regulated by moderate noise intensity to track the rhythm of subthreshold pacemaker, indicating the occurrence of stochastic resonance (SR) in the considered neural system. Inheriting the characteristics of two types of synapses-electrical and chemical ones, neural networks with hybrid electrical-chemical synapses are of great improvement in neuron communication. Particularly, chemical synapses are conducive to increase the network detectability by lowering the resonance noise intensity, while the information is better transmitted through the networks via electrical coupling. Moreover, time delay is able to enhance or destroy the periodic stochastic response behavior intermittently. In the time-delayed small-world neuronal networks, the introduction of electrical synapses can significantly improve the signal detection capability by widening the range of optimal noise intensity for the subthreshold signal, and the efficiency of SR is largely amplified in the case of pure chemical couplings. In addition, the stochastic response behavior is also profoundly influenced by the network topology. Increasing the rewiring probability in pure chemically coupled networks can always enhance the effect of SR, which is slightly influenced by information transmission delay. On the other hand, the capacity of information communication is robust to the network topology within the time-delayed neuronal systems including electrical couplings.
DEFF Research Database (Denmark)
Larsen, Jesper Abildgaard; Wisniewski, Rafal; Grunnet, Jacob Deleuran
2008-01-01
As initially suggested by E. Sontag, it is possible to approximate an arbitrary nonlinear system by a set of piecewise linear systems. In this work we concentrate on how to control a system given by a set of piecewise linear systems defined on simplices. By using the results of L. Habets and J. van...... Schuppen, it is possible to find a controller for the system on each of the simplices thus guaranteeing that the system flow on the simplex only will leave the simplex through a subset of its faces. Motivated by R. Forman, on the triangulated state space we define a combinatorial vector field, which...... indicates for a given face the future simplex. In the suggested definition we allow nondeterminacy in form of splitting and merging of solution trajectories. The combinatorial vector field gives rise to combinatorial counterparts of most concepts from dynamical systems, such as duals to vector fields, flow...
Fuzzy logic systems are equivalent to feedforward neural networks
Institute of Scientific and Technical Information of China (English)
李洪兴
2000-01-01
Fuzzy logic systems and feedforward neural networks are equivalent in essence. First, interpolation representations of fuzzy logic systems are introduced and several important conclusions are given. Then three important kinds of neural networks are defined, i.e. linear neural networks, rectangle wave neural networks and nonlinear neural networks. Then it is proved that nonlinear neural networks can be represented by rectangle wave neural networks. Based on the results mentioned above, the equivalence between fuzzy logic systems and feedforward neural networks is proved, which will be very useful for theoretical research or applications on fuzzy logic systems or neural networks by means of combining fuzzy logic systems with neural networks.
Institute of Scientific and Technical Information of China (English)
谢惠琴; 王全义
2004-01-01
In this paper, we study the existence, uniqueness, and the global exponential stability of the periodic solution and equilibrium of hybrid bidirectional associative memory neural networks with discrete delays. By ingeniously importing real parameters di > 0 (i = 1, 2,..., n) which can be adjusted, making use of the Lyapunov functional method and some analysis techniques, some new sufficient conditions are established. Our results generalize and improve the related results in [9]. These conditions can be used both to design globally exponentially stable and periodical oscillatory hybrid bidirectional associative neural networks with discrete delays, and to enlarge the area of designing neural networks. Our work has important significance in related theory and its application.
Hybrid2 - The hybrid power system simulation model
Energy Technology Data Exchange (ETDEWEB)
Baring-Gould, E.I.; Green, H.J.; Dijk, V.A.P. van [National Renewable Energy Lab., Golden, CO (United States); Manwell, J.F. [Univ. of Massachusetts, Amherst, MA (United States)
1996-12-31
There is a large-scale need and desire for energy in remote communities, especially in the developing world; however the lack of a user friendly, flexible performance prediction model for hybrid power systems incorporating renewables hindered the analysis of hybrids as options to conventional solutions. A user friendly model was needed with the versatility to simulate the many system locations, widely varying hardware configurations, and differing control options for potential hybrid power systems. To meet these ends, researchers from the National Renewable Energy Laboratory (NREL) and the University of Massachusetts (UMass) developed the Hybrid2 software. This paper provides an overview of the capabilities, features, and functionality of the Hybrid2 code, discusses its validation and future plans. Model availability and technical support provided to Hybrid2 users are also discussed. 12 refs., 3 figs., 4 tabs.
Neural Control of the Immune System
Sundman, Eva; Olofsson, Peder S.
2014-01-01
Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…
Neural Control of the Immune System
Sundman, Eva; Olofsson, Peder S.
2014-01-01
Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…
Adaptive control using a hybrid-neural model: application to a polymerisation reactor
Directory of Open Access Journals (Sweden)
Cubillos F.
2001-01-01
Full Text Available This work presents the use of a hybrid-neural model for predictive control of a plug flow polymerisation reactor. The hybrid-neural model (HNM is based on fundamental conservation laws associated with a neural network (NN used to model the uncertain parameters. By simulations, the performance of this approach was studied for a peroxide-initiated styrene tubular reactor. The HNM was synthesised for a CSTR reactor with a radial basis function neural net (RBFN used to estimate the reaction rates recursively. The adaptive HNM was incorporated in two model predictive control strategies, a direct synthesis scheme and an optimum steady state scheme. Tests for servo and regulator control showed excellent behaviour following different setpoint variations, and rejecting perturbations. The good generalisation and training capacities of hybrid models, associated with the simplicity and robustness characteristics of the MPC formulations, make an attractive combination for the control of a polymerisation reactor.
Symbolic Algorithmic Analysis of Rectangular Hybrid Systems
Institute of Scientific and Technical Information of China (English)
Hai-Bin Zhang; Zhen-Hua Duan
2009-01-01
This paper investigates symbolic algorithmic analysis of rectangular hybrid systems. To deal with the symbolic reachability problem, a restricted constraint system called hybrid zone is formalized for the representation and manipulation of rectangular automata state-spaces. Hybrid zones are proved to be closed over symbolic reachability operations of rectangular hybrid systems. They are also applied to model-checking procedures for verifying some important classes of timed computation tree logic formulas. To represent hybrid zones, a data structure called difference constraint matrix is defined.These enable us to deal with the symbolic algorithmic analysis of rectangular hybrid systems in an efficient way.
Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models
Güreşen, Erkam; Kayakutlu, Gülgün
Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurrent neural network (RNN), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) to extract new input variables. The comparison for each model is done in two view points: MSE and MAD using real exchange daily rate values of Istanbul Stock Exchange (ISE) index XU10).
Hybrid partial least squares and neural network approach for short-term electrical load forecasting
Institute of Scientific and Technical Information of China (English)
Shukang YANG; Ming LU; Huifeng XUE
2008-01-01
Intelligent systems and methods such as the neural network (NN) are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundant, and linearly or nonlinearly correlated with each other. Highly correlated input data can result in erroneous prediction results given out by an NN model. Besides this, the determination of the topological structure of an NN model has always been a problem for designers. This paper presents a new artificial intelligence hybrid procedure for next day electric load forecasting based on partial least squares (PLS) and NN. PLS is used for the compression of data input space, and helps to determine the structure of the NN model. The hybrid PLS-NN model can be used to predict hourly electric load on weekdays and weekends. The advantage of this methodology is that the hybrid model can provide faster convergence and more precise prediction results in comparison with abductive networks algorithm. Extensive testing on the electrical load data of the Puget power utility in the USA confirms the validity of the proposed approach.
Yang, Chih-Chung; Bose, N K
2005-05-01
Neural networks have been applied to landmine detection from data generated by different kinds of sensors. Real-valued neural networks have been used for detecting landmines from scattering parameters measured by ground penetrating radar (GPR) after disregarding phase information. This paper presents results using complex-valued neural networks, capable of phase-sensitive detection followed by classification. A two-layer hybrid neural network structure incorporating both supervised and unsupervised learning is proposed to detect and then classify the types of landmines. Tests are also reported on a benchmark data.
Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele
2014-01-01
The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved. PMID:24516363
Curcio, Stefano; Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele
2014-01-01
The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.
Parameter estimation in space systems using recurrent neural networks
Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.
1991-01-01
The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.
Nonlinear System Control Using Neural Networks
Directory of Open Access Journals (Sweden)
Jaroslava Žilková
2006-10-01
Full Text Available The paper is focused especially on presenting possibilities of applying off-linetrained artificial neural networks at creating the system inverse models that are used atdesigning control algorithm for non-linear dynamic system. The ability of cascadefeedforward neural networks to model arbitrary non-linear functions and their inverses isexploited. This paper presents a quasi-inverse neural model, which works as a speedcontroller of an induction motor. The neural speed controller consists of two cascadefeedforward neural networks subsystems. The first subsystem provides desired statorcurrent components for control algorithm and the second subsystem providescorresponding voltage components for PWM converter. The availability of the proposedcontroller is verified through the MATLAB simulation. The effectiveness of the controller isdemonstrated for different operating conditions of the drive system.
Digital systems for artificial neural networks
Energy Technology Data Exchange (ETDEWEB)
Atlas, L.E. (Interactive Systems Design Lab., Univ. of Washington, WA (US)); Suzuki, Y. (NTT Human Interface Labs. (US))
1989-11-01
A tremendous flurry of research activity has developed around artificial neural systems. These systems have also been tested in many applications, often with positive results. Most of this work has taken place as digital simulations on general-purpose serial or parallel digital computers. Specialized neural network emulation systems have also been developed for more efficient learning and use. The authors discussed how dedicated digital VLSI integrated circuits offer the highest near-term future potential for this technology.
Institute of Scientific and Technical Information of China (English)
郑生荣; 陈敏
2005-01-01
背景:塑料注塑成型技术可用于康复工程的多种实用矫形器中,遗传算法所表现出的易于实现及健壮性等特点,使它在许多领域,特别是近年来在机器学习、模式识别、智能控制和最优化等领域得到了广泛应用.目的:建立基于混合神经网络与遗传算法方法的注塑工艺参数优化系统.设计:开发性研究.单位:南昌工程学院机电工程系及南昌铁路中心医院干部病房.方法:用Matlab语言编制了应用程序,对神经网络的参数预测与遗传算法的优化过程进行求解.将网络预测结果与CAE(computer aided engineering)模拟结果进行比较和误差分析,显示出反向传播网络的稳定性和可靠性.结果:优化结果经CAE模拟和实验验证,证明是正确的,结论:基于混合神经网络与遗传算法方法的注塑工艺参数优化方法是可行的.该优化系统具有工程实用价值.%BACKGROUND: Injection molding can be used in producing a variety of orthotic devices of practical use in rehabilitation medicine. With its merits of convenient realization and stability, genetic algorism(GA) has found wide application in many fields, especially in machine learning, pattern recognition, intelligent control and optimization.OBJECTIVE: To establish an optimization system for injection molding parameters based on hybrid neural network and genetic algorithm.DESIGN: An open trial.SETTING: Department of Mechanical and Electrical Engineering, Nanchang Institute of Technology; Ward for Cadres, Nanchang Railway Central Hospital.METHODS: The application program was developed in Matlab language and used in the parameter prediction with the neural network and genetic algorithm optimization. The comparison and error analysis were made between the neural network-predicted results and those generated by simulation with computer aided engineering (CAE), which showed that the back propagation network was stable and reliable.RESULTS: The results of
A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics
Kobayashi, Takahisa; Simon, Donald L.
2001-01-01
In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.
IMPULSE CONTROL HYBRID ELECTRICAL SYSTEM
Directory of Open Access Journals (Sweden)
A. A. Lobaty
2016-01-01
Full Text Available This paper extends the recently introduced approach for modeling and solving the optimal control problem of fixedswitched mode DC-DC power converter. DCDC converters are a class of electric power circuits that used extensively in regulated DC power supplies, DC motor drives of different types, in Photovoltaic Station energy conversion and other applications due to its advantageous features in terms of size, weight and reliable performance. The main problem in controlling this type converters is in their hybrid nature as the switched circuit topology entails different modes of operation, each of it with its own associated linear continuous-time dynamics.This paper analyses the modeling and controller synthesis of the fixed-frequency buck DC-DC converter, in which the transistor switch is operated by a pulse sequence with constant frequency. In this case the regulation of the DC component of the output voltage is via the duty cycle. The optimization of the control system is based on the formation of the control signal at the output.It is proposed to solve the problem of optimal control of a hybrid system based on the formation of the control signal at the output of the controller, which minimizes a given functional integral quality, which is regarded as a linear quadratic Letov-Kalman functional. Search method of optimal control depends on the type of mathematical model of control object. In this case, we consider a linear deterministic model of the control system, which is common for the majority of hybrid electrical systems. For this formulation of the optimal control problem of search is a problem of analytical design of optimal controller, which has the analytical solution.As an example of the hybrid system is considered a step-down switching DC-DC converter, which is widely used in various electrical systems: as an uninterruptible power supply, battery charger for electric vehicles, the inverter in solar photovoltaic power plants.. A
Hybrid slab-microchannel gel electrophoresis system
Balch, Joseph W.; Carrano, Anthony V.; Davidson, James C.; Koo, Jackson C.
1998-01-01
A hybrid slab-microchannel gel electrophoresis system. The hybrid system permits the fabrication of isolated microchannels for biomolecule separations without imposing the constraint of a totally sealed system. The hybrid system is reusable and ultimately much simpler and less costly to manufacture than a closed channel plate system. The hybrid system incorporates a microslab portion of the separation medium above the microchannels, thus at least substantially reducing the possibility of non-uniform field distribution and breakdown due to uncontrollable leakage. A microslab of the sieving matrix is built into the system by using plastic spacer materials and is used to uniformly couple the top plate with the bottom microchannel plate.
Hybrid neural modelling of an anaerobic digester with respect to biological constraints.
Karama, A; Bernard, O; Gouzé, J L; Benhammou, A; Dochain, D
2001-01-01
A hybrid model for an anaerobic digestion process is proposed. The fermentation is assumed to be performed in two steps, acidogenesis and methanogenesis, by two bacterial populations. The model is based on mass balance equations, and the bacterial growth rates are represented by neural networks. In order to guarantee the biological meaning of the hybrid model (positivity of the concentrations, boundedness, saturation or inhibition of the growth rates) outside the training data set, a method that imposes constraints in the neural network is proposed. The method is applied to experimental data from a fixed bed reactor.
Institute of Scientific and Technical Information of China (English)
Tang Yang; Zhong Hui-Huang; Fang Jian-An
2008-01-01
A general model of linearly stochastically coupled identical connected neural networks with hybrid coupling is proposed,which is composed of constant coupling,coupling discrete time-varying delay and coupling distributed timevarying delay.All the coupling terms are subjected to stochastic disturbances described in terms of Brownian motion,which reflects a more realistic dynamical behaviour of coupled systems in practice.Based on a simple adaptive feedback controller and stochastic stability theory,several sufficient criteria are presented to ensure the synchronization of linearly stochastically coupled complex networks with coupling mixed time-varying delays.Finally,numerical simulatious illustrated by scale-free complex networks verify the effectiveness of the proposed controllers.
Quantum technologies with hybrid systems.
Kurizki, Gershon; Bertet, Patrice; Kubo, Yuimaru; Mølmer, Klaus; Petrosyan, David; Rabl, Peter; Schmiedmayer, Jörg
2015-03-31
An extensively pursued current direction of research in physics aims at the development of practical technologies that exploit the effects of quantum mechanics. As part of this ongoing effort, devices for quantum information processing, secure communication, and high-precision sensing are being implemented with diverse systems, ranging from photons, atoms, and spins to mesoscopic superconducting and nanomechanical structures. Their physical properties make some of these systems better suited than others for specific tasks; thus, photons are well suited for transmitting quantum information, weakly interacting spins can serve as long-lived quantum memories, and superconducting elements can rapidly process information encoded in their quantum states. A central goal of the envisaged quantum technologies is to develop devices that can simultaneously perform several of these tasks, namely, reliably store, process, and transmit quantum information. Hybrid quantum systems composed of different physical components with complementary functionalities may provide precisely such multitasking capabilities. This article reviews some of the driving theoretical ideas and first experimental realizations of hybrid quantum systems and the opportunities and challenges they present and offers a glance at the near- and long-term perspectives of this fascinating and rapidly expanding field.
Quantum technologies with hybrid systems
Kurizki, Gershon; Bertet, Patrice; Kubo, Yuimaru; Mølmer, Klaus; Petrosyan, David; Rabl, Peter; Schmiedmayer, Jörg
2015-01-01
An extensively pursued current direction of research in physics aims at the development of practical technologies that exploit the effects of quantum mechanics. As part of this ongoing effort, devices for quantum information processing, secure communication, and high-precision sensing are being implemented with diverse systems, ranging from photons, atoms, and spins to mesoscopic superconducting and nanomechanical structures. Their physical properties make some of these systems better suited than others for specific tasks; thus, photons are well suited for transmitting quantum information, weakly interacting spins can serve as long-lived quantum memories, and superconducting elements can rapidly process information encoded in their quantum states. A central goal of the envisaged quantum technologies is to develop devices that can simultaneously perform several of these tasks, namely, reliably store, process, and transmit quantum information. Hybrid quantum systems composed of different physical components with complementary functionalities may provide precisely such multitasking capabilities. This article reviews some of the driving theoretical ideas and first experimental realizations of hybrid quantum systems and the opportunities and challenges they present and offers a glance at the near- and long-term perspectives of this fascinating and rapidly expanding field. PMID:25737558
Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms
Abraham, Ajith
2004-01-01
Evolutionary artificial neural networks (EANNs) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolutionary algorithms are used to adapt the connection weights, network architecture and learning algorithms according to the problem environment. Even though evolutionary algorithms are well known as efficient global search algorithms, very often they miss the best local solutions in the complex s...
Hybrid synchronization of hyperchaotic Lu system
Indian Academy of Sciences (India)
K Sebastian Sudheer; M Sabir
2009-10-01
In this paper, we study the hybrid synchronization between two identical hyperchaotic Lu systems. Hybrid synchronization of hyperchaotic Lu system is achieved through synchronization of two pairs of states and anti-synchronization of the other two pairs of states. Active controls are designed to achieve hybrid synchronization between drive and response systems using the sum and difference of relevant variables of the chaotic systems. Numerical simulations are presented to evaluate the analysis and effectiveness of the controllers.
From hybrid-media system to hybrid-media politicians
DEFF Research Database (Denmark)
Eberholst, Mads Kæmsgaard; Ørsten, Mark; Burkal, Rasmus
2017-01-01
An increasingly complex hybrid system of social- and traditional-news media surrounds Nordic election campaigns as politically experienced incumbents favour traditional news media, and younger, lesser-known candidates’ social media. Despite little evidence for hybrid-media politicians, politicians......’ media use is changing rapidly; 15%–16% of Danish candidates used Twitter in 2011 but 68% in 2015. In this large-sample content analysis, party leaders have high traditional-news-media and low Twitter presence, and younger candidates visa-versa, but some politicians have high presence in both. Hybrid...
Directory of Open Access Journals (Sweden)
T. Botmart
2013-01-01
Full Text Available The problem of guaranteed cost control for exponential synchronization of cellular neural networks with interval nondifferentiable and distributed time-varying delays via hybrid feedback control is considered. The interval time-varying delay function is not necessary to be differentiable. Based on the construction of improved Lyapunov-Krasovskii functionals is combined with Leibniz-Newton's formula and the technique of dealing with some integral terms. New delay-dependent sufficient conditions for the exponential synchronization of the error systems with memoryless hybrid feedback control are first established in terms of LMIs without introducing any free-weighting matrices. The optimal guaranteed cost control with linear error hybrid feedback is turned into the solvable problem of a set of LMIs. A numerical example is also given to illustrate the effectiveness of the proposed method.
Control for a class of hybrid systems
J.H. van Schuppen (Jan)
1997-01-01
textabstractA hybrid control system is a control theoretic model for a computer controlled engineering system. A definition of a hybrid control system is formulated that consists of a product of a finite state automaton and of a family of continuous control systems. An example of a transportation
Hybrid computing using a neural network with dynamic external memory.
Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago; Agapiou, John; Badia, Adrià Puigdomènech; Hermann, Karl Moritz; Zwols, Yori; Ostrovski, Georg; Cain, Adam; King, Helen; Summerfield, Christopher; Blunsom, Phil; Kavukcuoglu, Koray; Hassabis, Demis
2016-10-27
Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.
Directory of Open Access Journals (Sweden)
Idris Khan
2017-01-01
Full Text Available High concentration of greenhouse gases in the atmosphere has increased dependency on photovoltaic (PV power, but its random nature poses a challenge for system operators to precisely predict and forecast PV power. The conventional forecasting methods were accurate for clean weather. But when the PV plants worked under heavy haze, the radiation is negatively impacted and thus reducing PV power; therefore, to deal with haze weather, Air Quality Index (AQI is introduced as a parameter to predict PV power. AQI, which is an indication of how polluted the air is, has been known to have a strong correlation with power generated by the PV panels. In this paper, a hybrid method based on the model of conventional back propagation (BP neural network for clear weather and BP AQI model for haze weather is used to forecast PV power with conventional parameters like temperature, wind speed, humidity, solar radiation, and an extra parameter of AQI as input. The results show that the proposed method has less error under haze condition as compared to conventional model of neural network.
NEURAL NETWORK SYSTEM FOR DIAGNOSTICS OF AVIATION DESIGNATION PRODUCTS
Directory of Open Access Journals (Sweden)
В. Єременко
2011-02-01
Full Text Available In the article for solving the classification problem of the technical state of the object, proposed to use a hybrid neural network with a Kohonen layer and multilayer perceptron. The information-measuring system can be used for standardless diagnostics, cluster analysis and to classify the products which made from composite materials. The advantage of this architecture is flexibility, high performance, ability to use different methods for collecting diagnostic information about unit under test, high reliability of information processing
Solar-geothermal hybrid system
Energy Technology Data Exchange (ETDEWEB)
Lentz, Alvaro; Almanza, Rafael [Instituto de Ingenieria, UNAM, Ciudad Universitaria, Edificio 12, 04510 Mexico DF (Mexico)
2006-10-15
The Cerro Prieto Geothermal Power Plant is located in the northwest of Mexico, lat. 32{sup o}39', long. 115{sup o}21' in the northern hemisphere. A solar-geothermal hybrid system is proposed in order to increase the steam flow during the present geothermal cycle, adding a solar field of parabolic trough concentrators. Energy is supplied to the geothermal flow from wells in order to increase the steam generation rate. This configuration will increase the capacity factor of the system by generating additional steam during the peak demand hours. The parabolic trough solar field is evaluated in North-South and East-West orientation collector alignments. A proposal to obtain an increase of 10% in steam flow is evaluated, as the increase in flow is limited by the content of dissolved salts, so as to avoid a liquid phase with high salt concentrations. The size of the parabolic troughs field was obtained. (author)
Consensus of Hybrid Multi-Agent Systems.
Zheng, Yuanshi; Ma, Jingying; Wang, Long
2017-01-27
In this brief, we consider the consensus problem of hybrid multiagent systems. First, the hybrid multiagent system is proposed, which is composed of continuous-time and discrete-time dynamic agents. Then, three kinds of consensus protocols are presented for the hybrid multiagent system. The analysis tool developed in this brief is based on the matrix theory and graph theory. With different restrictions of the sampling period, some necessary and sufficient conditions are established for solving the consensus of the hybrid multiagent system. The consensus states are also obtained under different protocols. Finally, simulation examples are provided to demonstrate the effectiveness of our theoretical results.
Hybrid Dynamical Systems Modeling, Stability, and Robustness
Goebel, Rafal; Teel, Andrew R
2012-01-01
Hybrid dynamical systems exhibit continuous and instantaneous changes, having features of continuous-time and discrete-time dynamical systems. Filled with a wealth of examples to illustrate concepts, this book presents a complete theory of robust asymptotic stability for hybrid dynamical systems that is applicable to the design of hybrid control algorithms--algorithms that feature logic, timers, or combinations of digital and analog components. With the tools of modern mathematical analysis, Hybrid Dynamical Systems unifies and generalizes earlier developments in continuous-time and discret
Stochastic Reachability Analysis of Hybrid Systems
Bujorianu, Luminita Manuela
2012-01-01
Stochastic reachability analysis (SRA) is a method of analyzing the behavior of control systems which mix discrete and continuous dynamics. For probabilistic discrete systems it has been shown to be a practical verification method but for stochastic hybrid systems it can be rather more. As a verification technique SRA can assess the safety and performance of, for example, autonomous systems, robot and aircraft path planning and multi-agent coordination but it can also be used for the adaptive control of such systems. Stochastic Reachability Analysis of Hybrid Systems is a self-contained and accessible introduction to this novel topic in the analysis and development of stochastic hybrid systems. Beginning with the relevant aspects of Markov models and introducing stochastic hybrid systems, the book then moves on to coverage of reachability analysis for stochastic hybrid systems. Following this build up, the core of the text first formally defines the concept of reachability in the stochastic framework and then...
Directory of Open Access Journals (Sweden)
Weide Li
2017-01-01
Full Text Available Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD, seasonal adjustment (S, cross validation (C, general regression neural network (GRNN and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR. The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW and Victorian State (VIC in Australia. Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.
Hybrid and Electric Advanced Vehicle Systems Simulation
Beach, R. F.; Hammond, R. A.; Mcgehee, R. K.
1985-01-01
Predefined components connected to represent wide variety of propulsion systems. Hybrid and Electric Advanced Vehicle System (HEAVY) computer program is flexible tool for evaluating performance and cost of electric and hybrid vehicle propulsion systems. Allows designer to quickly, conveniently, and economically predict performance of proposed drive train.
Training Artificial Neural Networks by a Hybrid PSO-CS Algorithm
Directory of Open Access Journals (Sweden)
Jeng-Fung Chen
2015-06-01
Full Text Available Presenting a satisfactory and efficient training algorithm for artificial neural networks (ANN has been a challenging task in the supervised learning area. Particle swarm optimization (PSO is one of the most widely used algorithms due to its simplicity of implementation and fast convergence speed. On the other hand, Cuckoo Search (CS algorithm has been proven to have a good ability for finding the global optimum; however, it has a slow convergence rate. In this study, a hybrid algorithm based on PSO and CS is proposed to make use of the advantages of both PSO and CS algorithms. The proposed hybrid algorithm is employed as a new training method for feedforward neural networks (FNNs. To investigate the performance of the proposed algorithm, two benchmark problems are used and the results are compared with those obtained from FNNs trained by original PSO and CS algorithms. The experimental results show that the proposed hybrid algorithm outperforms both PSO and CS in training FNNs.
Bressloff, Paul C
2015-01-01
We consider applications of path-integral methods to the analysis of a stochastic hybrid model representing a network of synaptically coupled spiking neuronal populations. The state of each local population is described in terms of two stochastic variables, a continuous synaptic variable and a discrete activity variable. The synaptic variables evolve according to piecewise-deterministic dynamics describing, at the population level, synapses driven by spiking activity. The dynamical equations for the synaptic currents are only valid between jumps in spiking activity, and the latter are described by a jump Markov process whose transition rates depend on the synaptic variables. We assume a separation of time scales between fast spiking dynamics with time constant [Formula: see text] and slower synaptic dynamics with time constant τ. This naturally introduces a small positive parameter [Formula: see text], which can be used to develop various asymptotic expansions of the corresponding path-integral representation of the stochastic dynamics. First, we derive a variational principle for maximum-likelihood paths of escape from a metastable state (large deviations in the small noise limit [Formula: see text]). We then show how the path integral provides an efficient method for obtaining a diffusion approximation of the hybrid system for small ϵ. The resulting Langevin equation can be used to analyze the effects of fluctuations within the basin of attraction of a metastable state, that is, ignoring the effects of large deviations. We illustrate this by using the Langevin approximation to analyze the effects of intrinsic noise on pattern formation in a spatially structured hybrid network. In particular, we show how noise enlarges the parameter regime over which patterns occur, in an analogous fashion to PDEs. Finally, we carry out a [Formula: see text]-loop expansion of the path integral, and use this to derive corrections to voltage-based mean-field equations, analogous
The quantum human central neural system.
Alexiou, Athanasios; Rekkas, John
2015-01-01
In this chapter we present Excess Entropy Production for human aging system as the sum of their respective subsystems and electrophysiological status. Additionally, we support the hypothesis of human brain and central neural system quantumness and we strongly suggest the theoretical and philosophical status of human brain as one of the unknown natural Dirac magnetic monopoles placed in the center of a Riemann sphere.
Some Applications of Spiking Neural P Systems
Mihai Ionescu; Dragoş Sburlan
2012-01-01
In this paper we investigate some applications of spiking neural P systems regarding their capability to solve some classical computer science problems. In this respect versatility of such systems is studied to simulate a well known parallel computational model, namely the Boolean circuits. In addition, another notorious application -- sorting -- is considered within this framework.
Kobayashi, Takahisa; Simon, Donald L.
2002-01-01
As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.
Behavioral Modeling of a C-Band Ring Hybrid Coupler Using Artificial Neural Networks
Directory of Open Access Journals (Sweden)
E. Demircioglu
2010-12-01
Full Text Available Artificial Neural Networks (ANNs gained importance on the RF microwave (MW design area and behavioral modeling of MW components in the past few decades. This paper presents a cost effective neural network (NN approach to overcome design, modeling and optimization problems of an 180deg ring hybrid coupler operating in C-Band. The proposed NN model is trained by data sets obtained from electromagnetic (EM simulators and neural test results are compared with simulator findings to determine the network accuracy. Moreover, necessary trade-offs are applied to improve the networks’ performance. Finally correlation factors, which are defined as comparison criteria between EM-simulator and proposed neural models, are calculated for each trade-off case.
Hybrid internal model control and proportional control of chaotic dynamical systems
Institute of Scientific and Technical Information of China (English)
齐冬莲; 姚良宾
2004-01-01
A new chaos control method is proposed to take advantage of chaos or avoid it. The hybrid Internal Model Control and Proportional Control learning scheme are introduced. In order to gain the desired robust performance and ensure the system's stability, Adaptive Momentum Algorithms are also developed. Through properly designing the neural network plant model and neural network controller, the chaotic dynamical systems are controlled while the parameters of the BP neural network are modified. Taking the Lorenz chaotic system as example, the results show that chaotic dynamical systems can be stabilized at the desired orbits by this control strategy.
Hybrid internal model control and proportional control of chaotic dynamical systems.
Qi, Dong-lian; Yao, Liang-bin
2004-01-01
A new chaos control method is proposed to take advantage of chaos or avoid it. The hybrid Internal Model Control and Proportional Control learning scheme are introduced. In order to gain the desired robust performance and ensure the system's stability, Adaptive Momentum Algorithms are also developed. Through properly designing the neural network plant model and neural network controller, the chaotic dynamical systems are controlled while the parameters of the BP neural network are modified. Taking the Lorenz chaotic system as example, the results show that chaotic dynamical systems can be stabilized at the desired orbits by this control strategy.
Symmetry reduction for stochastic hybrid systems
Bujorianu, L.M.; Katoen, J.P.
2009-01-01
This paper is focused on adapting symmetry reduction, a technique that is highly successful in traditional model checking, to stochastic hybrid systems. We first show that performability analysis of stochastic hybrid systems can be reduced to a stochastic reachability analysis (SRA). Then, we genera
Symmetry Reduction For Stochastic Hybrid Systems
Bujorianu, L.M.; Katoen, J.P.
2008-01-01
This paper is focused on adapting symmetry reduction, a technique that is highly successful in traditional model checking, to stochastic hybrid systems. To that end, we first show that performability analysis of stochastic hybrid systems can be reduced to a stochastic reachability analysis (SRA). Th
Hybrid logics with infinitary proof systems
Kooi, Barteld; Renardel de Lavalette, Gerard; Verbrugge, Rineke
2006-01-01
We provide a strongly complete infinitary proof system for hybrid logic. This proof system can be extended with countably many sequents. Thus, although these logics may be non-compact, strong completeness proofs are provided for infinitary hybrid versions of non-compact logics like ancestral logic a
Statistical Model Checking for Stochastic Hybrid Systems
DEFF Research Database (Denmark)
David, Alexandre; Du, Dehui; Larsen, Kim Guldstrand
2012-01-01
This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique ap...
Fault tolerant control design for hybrid systems
Energy Technology Data Exchange (ETDEWEB)
Yang, Hao; Jiang, Bin [Nanjing University of Aeronautics and Astronautics, Nanjing (China); Cocquempot, Vincent [Universite des Sciences et Technologies de Lille, Villeneuve d' Ascq (France)
2010-07-01
This book intends to provide the readers a good understanding on how to achieve Fault Tolerant Control goal of Hybrid Systems. The book can be used as a reference for the academic research on Fault Tolerant Control and Hybrid Systems or used in Ph.D. study of control theory and engineering. The knowledge background for this monograph would be some undergraduate and graduate courses on Fault Diagnosis and Fault Tolerant Control theory, linear system theory, nonlinear system theory, Hybrid Systems theory and Discrete Event System theory. (orig.)
Spiking neural P systems with multiple channels.
Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian
2017-11-01
Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.
Kannada character recognition system using neural network
Kumar, Suresh D. S.; Kamalapuram, Srinivasa K.; Kumar, Ajay B. R.
2013-03-01
Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. As there is no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India. In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten Kannada character is resized into 20x30 Pixel. The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different Kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems.
Recent advances on hybrid approaches for designing intelligent systems
Melin, Patricia; Pedrycz, Witold; Kacprzyk, Janusz
2014-01-01
This book describes recent advances on hybrid intelligent systems using soft computing techniques for diverse areas of application, such as intelligent control and robotics, pattern recognition, time series prediction and optimization complex problems. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of type-2 fuzzy logic, which basically consists of papers that propose new models and applications for type-2 fuzzy systems. The second part contains papers with the main theme of bio-inspired optimization algorithms, which are basically papers using nature-inspired techniques to achieve optimization of complex optimization problems in diverse areas of application. The third part contains pape...
Radial basis function neural network for power system load-flow
Energy Technology Data Exchange (ETDEWEB)
Karami, A.; Mohammadi, M.S. [Faculty of Engineering, The University of Guilan, P.O. Box 41635-3756, Rasht (Iran)
2008-01-15
This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)
Integrated Neural Flight and Propulsion Control System
Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)
2001-01-01
This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.
Directory of Open Access Journals (Sweden)
Lukas Falat
2016-01-01
Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Falat, Lukas; Marcek, Dusan; Durisova, Maria
2016-01-01
This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Hybrid neural network model for the design of beam subjected to bending and shear
Indian Academy of Sciences (India)
H Sudarsana Rao; B Ramesh Babu
2007-10-01
There is no direct method for design of beams. In general the dimensions of the beam and reinforcement are initially assumed and then the interaction formula is used to verify the suitability of chosen dimensions. This approach necessitates few trials for coming up with an economical and safe design. This paper demonstrates the applicability of Artiﬁcial Neural Networks (ANN) and Genetic Algorithms (GA) for the design of beams subjected to moment and shear. A hybrid neural network model which combines the features of feed forward neural networks and genetic algorithms has been developed for the design of beam subjected to moment and shear. The network has been trained with design data obtained from design experts in the ﬁeld. The hybrid neural network model learned the design of beam in just 1000 training cycles. After successful learning, the model predicted the depth of the beam, area of steel, spacing of stirrups required for new problems with accuracy satisfying all design constraints. The various stages involved in the development of a genetic algorithm based neural network model are addressed at length in this paper.
Institute of Scientific and Technical Information of China (English)
昝昕武; 平春蕾; 符欲梅
2011-01-01
Because of shutting down of the power supply, disturbance or noise in transmission, replacement of sensors, and etc. , the missing data is occurred frequently in bridge health monitoring system. This will affect the bridge health monitoring system to evaluate the state of bridge. So, it is very important to impute these missing data. In this paper, some missing data examples occurred in an actual bridge health monitoring system are listed. By means of time series' feature of bridge health monitoring system' s data and neural network' s powerful mapping capability, the paper proposed a hybrid model method of time series and a neural network. It could be utilized to forecast the missing data. In the paper, the hybrid model' s imputation results and time series' imputation results were compared. It shows that the hybrid model method can treat the missing data with a lower error.%列举了实际桥梁健康监测系统中数据缺失的几种形式,根据桥梁健康监测系统中监测数据是时间序列集的特点,以及神经网络强大的映射能力,利用神经网络及时间序列混合模型的方法来填补缺失数据,并将该方法与时间序列法的填补结果进行对比,结果表明该方法处理缺失数据的误差较低.
Hybrid dynamical systems observation and control
Defoort, Michael
2015-01-01
This book is a collection of contributions defining the state of current knowledge and new trends in hybrid systems – systems involving both continuous dynamics and discrete events – as described by the work of several well-known groups of researchers. Hybrid Dynamical Systems presents theoretical advances in such areas as diagnosability, observability and stabilization for various classes of system. Continuous and discrete state estimation and self-triggering control of nonlinear systems are advanced. The text employs various methods, among them, high-order sliding modes, Takagi–Sugeno representation and sampled-data switching to achieve its ends. The many applications of hybrid systems from power converters to computer science are not forgotten; studies of flexible-joint robotic arms and – as representative biological systems – the behaviour of the human heart and vasculature, demonstrate the wide-ranging practical significance of control in hybrid systems. The cross-disciplinary origins of study ...
Directory of Open Access Journals (Sweden)
Valentin Potapov
2016-12-01
Full Text Available Purpose: This work presents a method of diagnosing the technical condition of turbofan engines using hybrid neural network algorithm based on software developed for the analysis of data obtained in the aircraft life. Methods: allows the engine diagnostics with deep recognition to the structural assembly in the presence of single structural damage components of the engine running and the multifaceted damage. Results: of the optimization of neural network structure to solve the problems of evaluating technical state of the bypass turbofan engine, when used with genetic algorithms.
Nature-inspired design of hybrid intelligent systems
Castillo, Oscar; Kacprzyk, Janusz
2017-01-01
This book highlights recent advances in the design of hybrid intelligent systems based on nature-inspired optimization and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction, and optimization of complex problems. The book is divided into seven main parts, the first of which addresses theoretical aspects of and new concepts and algorithms based on type-2 and intuitionistic fuzzy logic systems. The second part focuses on neural network theory, and explores the applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The book’s third part presents enhancements to meta-heuristics based on fuzzy logic techniques and describes new nature-inspired optimization algorithms that employ fuzzy dynamic adaptation of parameters, while the fourth part presents diverse applications of nature-inspired optimization algorithms. In turn, the fifth part investigates applications of fuzzy logic in diverse areas, such as...
Neural circuits as computational dynamical systems.
Sussillo, David
2014-04-01
Many recent studies of neurons recorded from cortex reveal complex temporal dynamics. How such dynamics embody the computations that ultimately lead to behavior remains a mystery. Approaching this issue requires developing plausible hypotheses couched in terms of neural dynamics. A tool ideally suited to aid in this question is the recurrent neural network (RNN). RNNs straddle the fields of nonlinear dynamical systems and machine learning and have recently seen great advances in both theory and application. I summarize recent theoretical and technological advances and highlight an example of how RNNs helped to explain perplexing high-dimensional neurophysiological data in the prefrontal cortex.
Advanced propulsion system concept for hybrid vehicles
Bhate, S.; Chen, H.; Dochat, G.
1980-01-01
A series hybrid system, utilizing a free piston Stirling engine with a linear alternator, and a parallel hybrid system, incorporating a kinematic Stirling engine, are analyzed for various specified reference missions/vehicles ranging from a small two passenger commuter vehicle to a van. Parametric studies for each configuration, detail tradeoff studies to determine engine, battery and system definition, short term energy storage evaluation, and detail life cycle cost studies were performed. Results indicate that the selection of a parallel Stirling engine/electric, hybrid propulsion system can significantly reduce petroleum consumption by 70 percent over present conventional vehicles.
Lo, Joseph Y.; Baydush, Alan H.; Floyd, Carey E., Jr.
1994-05-01
We developed a hybrid artificial neural network for scatter compensation in digital portable chest radiographs. The network inputs an image region of interest (ROI), and outputs the scatter estimate at the ROI's center. We segmented each image into four regions by relative detected exposure, then trained a separate Adaline (adaptive linear element) or adaptive filter for each region. We produced a spatially varying hybrid Madaline (mulitple Adaline) by combining outputs from weight matrices of different sizes trained for different durations. The network was trained with 20 patient or 1280 examples, then evaluated with another 5 patients or 320 examples. Scatter estimation errors were not very different, ranging from the Adaline's 6.9 percent to the hybrid Madaline's 5.5 percent. Primary errors (more relevant to quantitative radiography techniques like dual energy imaging) were 43 percent for the Adaline, reduced to 27 percent for the Madaline, and further reduced to 19 percent for the hybrid Madaline. The trained weight matrices, which act like convolution filters, resembled the shape and magnitude of scatter point spread functions. All networks outperformed conventional convolution-subraction techniques using analytical kernels. With its spatially varying neural network model, the hybrid Madaline provided the most accurate and robust estimation of scatter and primary exposures.
Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition
Melin, Patricia
2012-01-01
This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...
Hybrid robot climbing system design
Purna Irawan, Agustinus; Halim, Agus; Kurniawan, Hengky
2017-09-01
This research aims to develop a climbing hybrid robot, especially to design the structure of robot that quite strong and how to build an optimal mechanism for transmitting the motor’s rotation and torque to generate movement up the pole. In this research we use analytical methods using analysis software, simulation, a prototype, and robot trial. The result showed that robot could climb a pole by with maximum velocity 0.33m/s with a 20 kg load. Based on a weight diversity trial between 10 kg and 20 kg we obtained climb up load factor with value 0.970 ± 0.0223 and climb down load factor with value 0.910 ± 0.0163. Displacement of the frame structure was 7.58 mm. To minimize this displacement, the gate system was used so as to optimize the gripper while gripping the pole. The von Misses stress in the roller was 48.49 MPa, with 0.12 mm of displacement. This result could be a reference for robot development in further research.
A Hybrid Neural Network Prediction Model of Air Ticket Sales
Directory of Open Access Journals (Sweden)
Han-Chen Huang
2013-11-01
Full Text Available Air ticket sales revenue is an important source of revenue for travel agencies, and if future air ticket sales revenue can be accurately forecast, travel agencies will be able to advance procurement to achieve a sufficient amount of cost-effective tickets. Therefore, this study applied the Artificial Neural Network (ANN and Genetic Algorithms (GA to establish a prediction model of travel agency air ticket sales revenue. By verifying the empirical data, this study proved that the established prediction model has accurate prediction power, and MAPE (mean absolute percentage error is only 9.11%. The established model can provide business operators with reliable and efficient prediction data as a reference for operational decisions.
Safety Verification for Probabilistic Hybrid Systems
DEFF Research Database (Denmark)
Zhang, Lijun; She, Zhikun; Ratschan, Stefan;
2010-01-01
The interplay of random phenomena and continuous real-time control deserves increased attention for instance in wireless sensing and control applications. Safety verification for such systems thus needs to consider probabilistic variations of systems with hybrid dynamics. In safety verification...... hybrid systems and develop a general abstraction technique for verifying probabilistic safety problems. This gives rise to the first mechanisable technique that can, in practice, formally verify safety properties of non-trivial continuous-time stochastic hybrid systems-without resorting to point...... of classical hybrid systems we are interested in whether a certain set of unsafe system states can be reached from a set of initial states. In the probabilistic setting, we may ask instead whether the probability of reaching unsafe states is below some given threshold. In this paper, we consider probabilistic...
Safety Verification for Probabilistic Hybrid Systems
DEFF Research Database (Denmark)
Zhang, Lijun; She, Zhikun; Ratschan, Stefan;
2012-01-01
The interplay of random phenomena and continuous dynamics deserves increased attention, especially in the context of wireless sensing and control applications. Safety verification for such systems thus needs to consider probabilistic variants of systems with hybrid dynamics. In safety verification...... hybrid systems and develop a general abstraction technique for verifying probabilistic safety problems. This gives rise to the first mechanisable technique that can, in practice, formally verify safety properties of non-trivial continuous-time stochastic hybrid systems. Moreover, being based...... of classical hybrid systems, we are interested in whether a certain set of unsafe system states can be reached from a set of initial states. In the probabilistic setting, we may ask instead whether the probability of reaching unsafe states is below some given threshold. In this paper, we consider probabilistic...
Hybrid Clustering-Classification Neural Network in the Medical Diagnostics of the Reactive Arthritis
Directory of Open Access Journals (Sweden)
Yevgeniy Bodyanskiy
2016-08-01
Full Text Available In the paper, the hybrid clustering-classification neural network is proposed. This network allows to increase a quality of information processing under the condition of overlapping classes due to the rational choice of learning rate parameter and introducing special procedure of fuzzy reasoning in the clustering-classification process, which occurs both with external learning signal ("supervised", and without one ("unsupervised". As similarity measure neighborhood function or membership one, cosine structures are used, which allow to provide a high flexibility due to self-learning-learning process and to provide some new useful properties. Many realized experiments have confirmed the efficiency of proposed hybrid clustering-classification neural network; also, this network was used for solving diagnostics task of reactive arthritis.
Adaptive neural-based fuzzy modeling for biological systems.
Wu, Shinq-Jen; Wu, Cheng-Tao; Chang, Jyh-Yeong
2013-04-01
The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems.
A Hybrid Neural Network and H-P Filter Model for Short-Term Vegetable Price Forecasting
Directory of Open Access Journals (Sweden)
Youzhu Li
2014-01-01
Full Text Available This paper is concerned with time series data for vegetable prices, which have a great impact on human’s life. An accurate forecasting method for prices and an early-warning system in the vegetable market are an urgent need in people’s daily lives. The time series price data contain both linear and nonlinear patterns. Therefore, neither a current linear forecasting nor a neural network can be adequate for modeling and predicting the time series data. The linear forecasting model cannot deal with nonlinear relationships, while the neural network model alone is not able to handle both linear and nonlinear patterns at the same time. The linear Hodrick-Prescott (H-P filter can extract the trend and cyclical components from time series data. We predict the linear and nonlinear patterns and then combine the two parts linearly to produce a forecast from the original data. This study proposes a structure of a hybrid neural network based on an H-P filter that learns the trend and seasonal patterns separately. The experiment uses vegetable prices data to evaluate the model. Comparisons with the autoregressive integrated moving average method and back propagation artificial neural network methods show that our method has higher accuracy than the others.
Statistical Model Checking for Stochastic Hybrid Systems
DEFF Research Database (Denmark)
David, Alexandre; Du, Dehui; Larsen, Kim Guldstrand
2012-01-01
This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique...... applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings....
Digital Repository Service at National Institute of Oceanography (India)
De, C.; Chakraborty, B.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 6, NO. 4, OCTOBER 2009 743 Acoustic Characterization of Seafloor Sediment Employing a Hybrid Method of Neural Network Architecture and Fuzzy Algorithm Chanchal De and Bishwajit Chakraborty Abstract... backscatter data [11]–[13] and side-scan sonar images [14]–[16] have been demonstrated for seafloor classification. In this letter, seafloor sediment is characterized using an unsupervised architecture called Kohonen’s self-organizing Manuscript received...
Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold
2014-12-01
In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature.
Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching
Directory of Open Access Journals (Sweden)
Asmau M. Ahmed
2017-07-01
Full Text Available Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1 The mixing process should occur at macroscopic level and (2 Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model. Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms.
Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels
Directory of Open Access Journals (Sweden)
Antonino Laudani
2015-01-01
Full Text Available A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.
Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment.
Chen, W C; Chang, Ni-Bin; Chen, Jeng-Chung
2003-01-01
Recent advances in control engineering suggest that hybrid control strategies, integrating some ideas and paradigms existing in different soft computing techniques, such as fuzzy logic, genetic algorithms, rough set theory, and neural networks, may provide improved control performance in wastewater treatment processes. This paper presents an innovative hybrid control algorithm leading to integrate the distinct aspects of indiscernibility capability of rough set theory and search capability of genetic algorithms with conventional neural-fuzzy controller design. The methodology proposed in this study employs a three-stage analysis that is designed in series for generating a representative state function, searching for a set of multi-objective control strategies, and performing a rough set-based autotuning for the neural-fuzzy logic controller to make it applicable for controlling an industrial wastewater treatment process. Research findings in the case study clearly indicate that the use of rough set theory to aid in the neural-fuzzy logic controller design can produce relatively better plant performance in terms of operating cost, control stability, and response time simultaneously, which is effective at least in the selected industrial wastewater treatment plant. Such a methodology is anticipated to be capable of dealing with many other types of process control problems in waste treatment processes by making only minor modifications.
Spiking neural P systems with weights.
Wang, Jun; Hoogeboom, Hendrik Jan; Pan, Linqiang; Păun, Gheorghe; Pérez-Jiménez, Mario J
2010-10-01
A variant of spiking neural P systems with positive or negative weights on synapses is introduced, where the rules of a neuron fire when the potential of that neuron equals a given value. The involved values-weights, firing thresholds, potential consumed by each rule-can be real (computable) numbers, rational numbers, integers, and natural numbers. The power of the obtained systems is investigated. For instance, it is proved that integers (very restricted: 1, -1 for weights, 1 and 2 for firing thresholds, and as parameters in the rules) suffice for computing all Turing computable sets of numbers in both the generative and the accepting modes. When only natural numbers are used, a characterization of the family of semilinear sets of numbers is obtained. It is shown that spiking neural P systems with weights can efficiently solve computationally hard problems in a nondeterministic way. Some open problems and suggestions for further research are formulated.
The labeled systems of multiple neural networks.
Nemissi, M; Seridi, H; Akdag, H
2008-08-01
This paper proposes an implementation scheme of K-class classification problem using systems of multiple neural networks. Usually, a multi-class problem is decomposed into simple sub-problems solved independently using similar single neural networks. For the reason that these sub-problems are not equivalent in their complexity, we propose a system that includes reinforced networks destined to solve complicated parts of the entire problem. Our approach is inspired from principles of the multi-classifiers systems and the labeled classification, which aims to improve performances of the networks trained by the Back-Propagation algorithm. We propose two implementation schemes based on both OAO (one-against-all) and OAA (one-against-one). The proposed models are evaluated using iris and human thigh databases.
IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA
Directory of Open Access Journals (Sweden)
KARAM M. Z. OTHMAN
2011-08-01
Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.
Hybrid model decomposition of speech and noise in a radial basis function neural model framework
DEFF Research Database (Denmark)
Sørensen, Helge Bjarup Dissing; Hartmann, Uwe
1994-01-01
applied is based on a combination of the hidden Markov model (HMM) decomposition method, for speech recognition in noise, developed by Varga and Moore (1990) from DRA and the hybrid (HMM/RBF) recognizer containing hidden Markov models and radial basis function (RBF) neural networks, developed by Singer...... and Lippmann (1992) from MIT Lincoln Lab. The present authors modified the hybrid recognizer to fit into the decomposition method to achieve high performance speech recognition in noisy environments. The approach has been denoted the hybrid model decomposition method and it provides an optimal method...... for decomposition of speech and noise by using a set of speech pattern models and a noise model(s), each realized as an HMM/RBF pattern model...
Simulating neural systems with Xyce.
Energy Technology Data Exchange (ETDEWEB)
Schiek, Richard Louis; Thornquist, Heidi K.; Mei, Ting; Warrender, Christina E.; Aimone, James Bradley; Teeter, Corinne; Duda, Alex M.
2012-12-01
Sandias parallel circuit simulator, Xyce, can address large scale neuron simulations in a new way extending the range within which one can perform high-fidelity, multi-compartment neuron simulations. This report documents the implementation of neuron devices in Xyce, their use in simulation and analysis of neuron systems.
Analysis of Hybrid Hydrogen Systems: Final Report
Energy Technology Data Exchange (ETDEWEB)
Dean, J.; Braun, R.; Munoz, D.; Penev, M.; Kinchin, C.
2010-01-01
Report on biomass pathways for hydrogen production and how they can be hybridized to support renewable electricity generation. Two hybrid systems were studied in detail for process feasibility and economic performance. The best-performing system was estimated to produce hydrogen at costs ($1.67/kg) within Department of Energy targets ($2.10/kg) for central biomass-derived hydrogen production while also providing value-added energy services to the electric grid.
Hopfield neural network based on ant system
Institute of Scientific and Technical Information of China (English)
洪炳镕; 金飞虎; 郭琦
2004-01-01
Hopfield neural network is a single layer feedforward neural network. Hopfield network requires some control parameters to be carefully selected, else the network is apt to converge to local minimum. An ant system is a nature inspired meta heuristic algorithm. It has been applied to several combinatorial optimization problems such as Traveling Salesman Problem, Scheduling Problems, etc. This paper will show an ant system may be used in tuning the network control parameters by a group of cooperated ants. The major advantage of this network is to adjust the network parameters automatically, avoiding a blind search for the set of control parameters.This network was tested on two TSP problems, 5 cities and 10 cities. The results have shown an obvious improvement.
Energy Technology Data Exchange (ETDEWEB)
Assadi, Mohsen; Fast, Magnus [Lund Inst. of Technology (Sweden). Dept. of Energy Sciences
2006-12-15
The project aim is to model the hybrid plant at Vaesthamnsverket in Helsingborg using artificial neural networks (ANN). The definition of a hybrid plant is that it uses more than one fuel, in this case a natural gas fuelled gas turbine with heat recovery steam generator (HRSG) and a biomass fuelled steam boiler with steam turbine. The models in the present project are based on operational data from the plant in contrary to previous projects where synthetic (simulated) data has been used in a large extent. The gas turbine represents the subsystem that has received most attention in previous projects, which will also be the case for the present project although models of the HRSG, steam boiler and steam turbine are created since the whole plant is of interest. The completed ANN sub modules are connected in a network, which can be used for e.g. offline simulation and real-time condition monitoring of the plant. A product, including all sub modules, is created in shape of a user-friendly interface in an MS Excel environment. This user interface can be used for continuous monitoring, training personnel and in planning of the operation. The target group is the plant owners and the original equipment manufacturers (OEM). The plant owners interest lies in receiving a product that can assist them when operating the plant, for instance supply them with information about the grade of degradation. The OEMs main interest lies in investigating the possibilities of delivering ANNs, based on synthetic data, along with their new gas turbines. Vaesthamnsverket have contributed with operational data from the plant as well as support in plant related questions. Siemens have contributed with expert knowledge about their gas turbine, the SGT800. The received data has been examined and filtered before used for training ANN models. The models have been evaluated with independent data. The results are very promising with ANN models showing high prediction accuracy. All subsystems can be
Institute for Brain and Neural Systems
2009-10-06
Cooper. A Probabilistic Model For Cursive Handwriting Recognition Using Spatial Context. ICASSP, Vol. 5, pp. 201-204, 2005. Technical Reports: 47 21...application to recognition of on-line cursive script. In Advances in Neural Information Processing Systems. Neskovic, P., Schuster, D., and Cooper, L. (2004...P., and Cooper, L. (2005c). A probabilistic model for cursive handwriting recognition using spatial context. In Proc. ICASSP. Wang, J., Neskovic, P
Multistage Hybrid Arabic/Indian Numeral OCR System
Alginaih, Yasser M
2010-01-01
The use of OCR in postal services is not yet universal and there are still many countries that process mail sorting manually. Automated Arabic/Indian numeral Optical Character Recognition (OCR) systems for Postal services are being used in some countries, but still there are errors during the mail sorting process, thus causing a reduction in efficiency. The need to investigate fast and efficient recognition algorithms/systems is important so as to correctly read the postal codes from mail addresses and to eliminate any errors during the mail sorting stage. The objective of this study is to recognize printed numerical postal codes from mail addresses. The proposed system is a multistage hybrid system which consists of three different feature extraction methods, i.e., binary, zoning, and fuzzy features, and three different classifiers, i.e., Hamming Nets, Euclidean Distance, and Fuzzy Neural Network Classifiers. The proposed system, systematically compares the performance of each of these methods, and ensures t...
Uncertainty Quantification in Hybrid Dynamical Systems
Sahai, Tuhin
2011-01-01
Uncertainty quantification (UQ) techniques are frequently used to ascertain output variability in systems with parametric uncertainty. Traditional algorithms for UQ are either system-agnostic and slow (such as Monte Carlo) or fast with stringent assumptions on smoothness (such as polynomial chaos and Quasi-Monte Carlo). In this work, we develop a fast UQ approach for hybrid dynamical systems by extending the polynomial chaos methodology to these systems. To capture discontinuities, we use a wavelet-based Wiener-Haar expansion. We develop a boundary layer approach to propagate uncertainty through separable reset conditions. We also introduce a transport theory based approach for propagating uncertainty through hybrid dynamical systems. Here the expansion yields a set of hyperbolic equations that are solved by integrating along characteristics. The solution of the partial differential equation along the characteristics allows one to quantify uncertainty in hybrid or switching dynamical systems. The above method...
Uncertainty quantification in hybrid dynamical systems
Sahai, Tuhin; Pasini, José Miguel
2013-03-01
Uncertainty quantification (UQ) techniques are frequently used to ascertain output variability in systems with parametric uncertainty. Traditional algorithms for UQ are either system-agnostic and slow (such as Monte Carlo) or fast with stringent assumptions on smoothness (such as polynomial chaos and Quasi-Monte Carlo). In this work, we develop a fast UQ approach for hybrid dynamical systems by extending the polynomial chaos methodology to these systems. To capture discontinuities, we use a wavelet-based Wiener-Haar expansion. We develop a boundary layer approach to propagate uncertainty through separable reset conditions. We also introduce a transport theory based approach for propagating uncertainty through hybrid dynamical systems. Here the expansion yields a set of hyperbolic equations that are solved by integrating along characteristics. The solution of the partial differential equation along the characteristics allows one to quantify uncertainty in hybrid or switching dynamical systems. The above methods are demonstrated on example problems.
Supermarket Refrigeration System - Benchmark for Hybrid System Control
DEFF Research Database (Denmark)
Sloth, Lars Finn; Izadi-Zamanabadi, Roozbeh; Wisniewski, Rafal
2007-01-01
This paper presents a supermarket refrigeration system as a benchmark for development of new ideas and a comparison of methods for hybrid systems' modeling and control. The benchmark features switch dynamics and discrete valued input making it a hybrid system, furthermore the outputs are subjected...
The endocannabinoid system drives neural progenitor proliferation.
Aguado, Tania; Monory, Krisztina; Palazuelos, Javier; Stella, Nephi; Cravatt, Benjamin; Lutz, Beat; Marsicano, Giovanni; Kokaia, Zaal; Guzmán, Manuel; Galve-Roperh, Ismael
2005-10-01
The discovery of multipotent neural progenitor (NP) cells has provided strong support for the existence of neurogenesis in the adult brain. However, the signals controlling NP proliferation remain elusive. Endocannabinoids, the endogenous counterparts of marijuana-derived cannabinoids, act as neuromodulators via presynaptic CB1 receptors and also control neural cell death and survival. Here we show that progenitor cells express a functional endocannabinoid system that actively regulates cell proliferation both in vitro and in vivo. Specifically, NPs produce endocannabinoids and express the CB1 receptor and the endocannabinoid-inactivating enzyme fatty acid amide hydrolase (FAAH). CB1 receptor activation promotes cell proliferation and neurosphere generation, an action that is abrogated in CB1-deficient NPs. Accordingly, proliferation of hippocampal NPs is increased in FAAH-deficient mice. Our results demonstrate that endocannabinoids constitute a new group of signaling cues that regulate NP proliferation and thus open novel therapeutic avenues for manipulation of NP cell fate in the adult brain.
Dynamic artificial neural networks with affective systems.
Schuman, Catherine D; Birdwell, J Douglas
2013-01-01
Artificial neural networks (ANNs) are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP) and long term depression (LTD), and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.
Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling
Directory of Open Access Journals (Sweden)
F. C. Corazza
2005-03-01
Full Text Available Neural networks and hybrid models were used to study substrate and product inhibition observed in the enzymatic hydrolysis of cellobiose at 40ºC, 50ºC and 55ºC, pH 4.8, using cellobiose solutions with or without the addition of exogenous glucose. Firstly, the initial velocity method and nonlinear fitting with StatisticaÒ were used to determine the kinetic parameters for either the uncompetitive or the competitive substrate inhibition model at a negligible product concentration and cellobiose from 0.4 to 2.0 g/L. Secondly, for six different models of substrate and product inhibitions and data for low to high cellobiose conversions in a batch reactor, neural networks were used for fitting the product inhibition parameter to the mass balance equations derived for each model. The two models found to be best were: 1 noncompetitive inhibition by substrate and competitive by product and 2 uncompetitive inhibition by substrate and competitive by product; however, these models’ correlation coefficients were quite close. To distinguish between them, hybrid models consisting of neural networks and first principle equations were used to select the best inhibition model based on the smallest norm observed, and the model with noncompetitive inhibition by substrate and competitive inhibition by product was shown to be the best predictor of cellobiose hydrolysis reactor behavior.
Dynamical systems, attractors, and neural circuits.
Miller, Paul
2016-01-01
Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.
Zainuddin, Zarita; Lai, Kee Huong; Ong, Pauline
2013-04-01
Artificial neural networks (ANNs) are powerful mathematical models that are used to solve complex real world problems. Wavelet neural networks (WNNs), which were developed based on the wavelet theory, are a variant of ANNs. During the training phase of WNNs, several parameters need to be initialized; including the type of wavelet activation functions, translation vectors, and dilation parameter. The conventional k-means and fuzzy c-means clustering algorithms have been used to select the translation vectors. However, the solution vectors might get trapped at local minima. In this regard, the evolutionary harmony search algorithm, which is capable of searching for near-optimum solution vectors, both locally and globally, is introduced to circumvent this problem. In this paper, the conventional k-means and fuzzy c-means clustering algorithms were hybridized with the metaheuristic harmony search algorithm. In addition to obtaining the estimation of the global minima accurately, these hybridized algorithms also offer more than one solution to a particular problem, since many possible solution vectors can be generated and stored in the harmony memory. To validate the robustness of the proposed WNNs, the real world problem of epileptic seizure detection was presented. The overall classification accuracy from the simulation showed that the hybridized metaheuristic algorithms outperformed the standard k-means and fuzzy c-means clustering algorithms.
Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong
2017-01-01
A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.
Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong
2017-01-01
A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model. PMID:28120889
Modular supervisory controller for hybrid power systems
Energy Technology Data Exchange (ETDEWEB)
Lemos Pereira, A. de
2000-06-01
The power supply of remote places has been commonly provided by thermal power plants, usually diesel generators. Although hybrid power systems may constitute the most economical solution in many applications their widespread application to the electrification schemes of remote areas still depends on improvements in the issues of design and operation control. The main limitations of the present hybrid power systems technology, which are identified in this work, are related to the control and supervision of the power system. Therefore this thesis focuses on the modularity of supervisory controllers in order to design cost-competitive and reliable hybrid power systems. The modular supervisory controller created in this project is considered an important part of a system design approach that aims to overcome the technical difficulties of the current engineering practice and contribute to open the market of hybrid power systems. The term modular refers to a set of design characteristics that allows the use of basically the same supervisory controller in different projects. The modularization and standardisation of the controller include several issues such as interfacing components, communication protocols, modelling, programming and control strategies. The modularity can reduce the highly specialised system engineering related to the integration of components, operation and control. It can also avoid the high costs for installation, service and maintenance. A modular algorithm for supervisory controllers has been developed (a Matlab program called SuperCon) using an object-oriented design and it has been tested through several simulations using different hybrid system configurations and different control strategies. This thesis presents a complete control system design process which can be used as the basis for the development and implementation of intelligent and autonomous supervisory controllers for hybrid power systems with modular characteristics. (au)
Nonlinear system identification and control based on modular neural networks.
Puscasu, Gheorghe; Codres, Bogdan
2011-08-01
A new approach for nonlinear system identification and control based on modular neural networks (MNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This is obtained using a partitioning algorithm. Each local nonlinear model is associated with a nonlinear controller. These are also implemented by neural networks. The switching between the neural controllers is done by a dynamical switcher, also implemented by neural networks, that tracks the different operating points. The proposed multiple modelling and control strategy has been successfully tested on simulated laboratory scale liquid-level system.
Hybrid feedback feedforward: An efficient design of adaptive neural network control.
Pan, Yongping; Liu, Yiqi; Xu, Bin; Yu, Haoyong
2016-04-01
This paper presents an efficient hybrid feedback feedforward (HFF) adaptive approximation-based control (AAC) strategy for a class of uncertain Euler-Lagrange systems. The control structure includes a proportional-derivative (PD) control term in the feedback loop and a radial-basis-function (RBF) neural network (NN) in the feedforward loop, which mimics the human motor learning control mechanism. At the presence of discontinuous friction, a sigmoid-jump-function NN is incorporated to improve control performance. The major difference of the proposed HFF-AAC design from the traditional feedback AAC (FB-AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as RBF-NN inputs. Yet, such a slight modification leads to several attractive properties of HFF-AAC, including the convenient choice of an approximation domain, the decrease of the number of RBF-NN inputs, and semiglobal practical asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach possesses the following two distinctive features: (i) all above attractive properties are achieved by a much simpler control scheme; (ii) the bounds of plant uncertainties are not required to be known. Consequently, the proposed approach guarantees a minimum configuration of the control structure and a minimum requirement of plant knowledge for the AAC design, which leads to a sharp decrease of implementation cost in terms of hardware selection, algorithm realization and system debugging. Simulation results have demonstrated that the proposed HFF-AAC can perform as good as or even better than the traditional FB-AAC under much simpler control synthesis and much lower computational cost.
Directory of Open Access Journals (Sweden)
G. N. Tiwari
2012-01-01
Full Text Available In this paper an attempt has been made to determine efficiency of semi transparent hybrid photovoltaic thermal double pass air collector for different PV technology and compare it with single pass air collector using artificial neural network (ANN technique for New Delhi weather station of India. The MATLAB 7.1 neural networks toolbox has been used for defining and training of ANN for determination of thermal, electrical, overall thermal and overall exergy efficiency of the system. The ANN model uses ambient air temperature, number of sunshine hours, number of clear days, temperature coefficient, cell efficiency, global and diffuse radiation as input parameters. The transfer function, neural network configuration and learning parameters have been selected based on highest convergence during training and testing of network. About 2000 sets of data from four weather stations (Bangalore, Mumbai, Srinagar and Jodhpur have been given as input for training and data of the fifth weather station (New Delhi has been used for testing purpose. It has been observed that the best transfer function for a given configuration is logsig. The feed forward back-propagation algorithm has been used in this analysis. Further the results of ANN model have been compared with analytical values on the basis of root mean square error.
Research and Design of a Fuzzy Neural Expert System
Institute of Scientific and Technical Information of China (English)
王仕军; 王树林
1995-01-01
We have developed a fuzzy neural expert system that has the precision and learning ability of a neural network.Knowledge is acquired from domain experts as fuzzy rules and membership functions.Then,they are converted into a neural network which implements fuzzy inference without rule matching.The neural network is applied to problem-solving and learns from the data obtained during operation to enhance the accuracy.The learning ability of the neural network makes it easy to modify the membership functions defined by domain experts.Also,by modifying the weights of neural networks adaptively,the problem of belief propagation in conventional expert systems can be solved easily.Converting the neural network back into fuzzy rules and membership functions helps explain the inner representation and operation of the neural network.
Rahmani, Mehran; Ghanbari, Ahmad; Ettefagh, Mir Mohammad
2016-12-01
This paper proposes a control scheme based on the fraction integral terminal sliding mode control and adaptive neural network. It deals with the system model uncertainties and the disturbances to improve the control performance of the Inchworm robot manipulator. A fraction integral terminal sliding mode control applies to the Inchworm robot manipulator to obtain the initial stability. Also, an adaptive neural network is designed to approximate the system uncertainties and unknown disturbances to reduce chattering phenomena. The weight matrix of the proposed adaptive neural network can be updated online, according to the current state error information. The stability of the proposed control method is proved by Lyapunov theory. The performance of the adaptive neural network fraction integral terminal sliding mode control is compared with three other conventional controllers such as sliding mode control, integral terminal sliding mode control and fraction integral terminal sliding mode control. Simulation results show the effectiveness of the proposed control method.
A "Hybrid" Approach for Synthesizing Optimal Controllers of Hybrid Systems
DEFF Research Database (Denmark)
Zhao, Hengjun; Zhan, Naijun; Kapur, Deepak
2012-01-01
We propose an approach to reduce the optimal controller synthesis problem of hybrid systems to quantifier elimination; furthermore, we also show how to combine quantifier elimination with numerical computation in order to make it more scalable but at the same time, keep arising errors due...... to discretization manageable and within bounds. A major advantage of our approach is not only that it avoids errors due to numerical computation, but it also gives a better optimal controller. In order to illustrate our approach, we use the real industrial example of an oil pump provided by the German company HYDAC...
Peripheral neural activity recording and stimulation system.
Loi, D; Carboni, C; Angius, G; Angotzi, G N; Barbaro, M; Raffo, L; Raspopovic, S; Navarro, X
2011-08-01
This paper presents a portable, embedded, microcontroller-based system for bidirectional communication (recording and stimulation) between an electrode, implanted in the peripheral nervous system, and a host computer. The device is able to record and digitize spontaneous and/or evoked neural activities and store them in data files on a PC. In addition, the system has the capability of providing electrical stimulation of peripheral nerves, injecting biphasic current pulses with programmable duration, intensity, and frequency. The recording system provides a highly selective band-pass filter from 800 Hz to 3 kHz, with a gain of 56 dB. The amplification range can be further extended to 96 dB with a variable gain amplifier. The proposed acquisition/stimulation circuitry has been successfully tested through in vivo measurements, implanting a tf-LIFE electrode in the sciatic nerve of a rat. Once implanted, the device showed an input referred noise of 0.83 μVrms, was capable of recording signals below 10 μ V, and generated muscle responses to injected stimuli. The results demonstrate the capability of processing and transmitting neural signals with very low distortion and with a power consumption lower than 1 W. A graphic, user-friendly interface has been developed to facilitate the configuration of the entire system, providing the possibility to activate stimulation and monitor recordings in real time.
Liquid fuel utilization in SOFC hybrid systems
Energy Technology Data Exchange (ETDEWEB)
Santin, Marco; Traverso, Alberto; Magistri, Loredana [TPG-DIMSET, University of Genoa, Via Montallegro 1, 16145 Genoa (Italy)
2009-10-15
The interest in solid oxide fuel cell systems comes from their capability of converting the chemical energy of traditional fuels into electricity, with high efficiency and low pollutant emissions. In this paper, a study of the design space of solid oxide fuel cell and gas turbine hybrids fed by methanol and kerosene is presented for stationary power generation in isolated areas (or transportation). A 500 kW class hybrid system was analysed using WTEMP original software developed by the Thermochemical Power Group of the University of Genoa. The choice of fuel-processing strategy and the influence of the main design parameters on the thermoeconomic characteristics of hybrid systems were investigated. The low capital and fuel cost of methanol systems make them the most attractive solutions among those investigated here. (author)
A Generic Hybrid Encryption System (HES
Directory of Open Access Journals (Sweden)
Ijaz Ali Shoukat
2013-03-01
Full Text Available This study proposes a Generic Hybrid Encryption System (HES under mutual committee of symmetric and asymmetric cryptosystems. Asymmetric (public key Cryptosystems associates several performance issues like computational incompetence, memory wastages, energy consumptions and employment limitations on bulky data sets but they are quite secure and reliable in key exchange over insecure remote communication channels. Symmetric (private key cryptosystems are 100 times out performed, having no such issues but they cannot fulfill non-repudiation, false modifications in secret key, fake modifications in cipher text and origin authentication of both parties while exchanging information. These contradictory issues can be omitted by utilizing hybrid encryption mechanisms (symmetric+asymmetric to get optimal benefits of both schemes. Several hybrid mechanisms are available with different logics but our logic differs in infrastructural design, simplicity, computational efficiency and security as compared to prior hybrid encryption schemes. Some prior schemes are either diversified in performance aspects, customer satisfaction, memory utilization or energy consumptions and some are vulnerable against forgery and password guessing (session key recovery attacks. We have done some functional and design related changes in existing Public Key Infrastructure (PKI to achieve simplicity, optimal privacy and more customer satisfaction by providing Hybrid Encryption System (HES that is able to fulfill all set of standardized security constraints. No such PKI based generic hybrid encryption scheme persists as we have provided in order to manage all these kinds of discussed issues.
Combined photovoltaic and thermal hybrid collector systems
Energy Technology Data Exchange (ETDEWEB)
Kern, E.C. Jr.; Russell, M.C.
1978-01-01
Solar energy collectors that produce both electric and thermal energy are an attractive alternative to individual thermal and photovoltaic collectors for certain applications and climates. Economic results from a system analysis indicate that hybrid collector systems are attractive in small buildings that have substantial heating loads. Passively cooled photovoltaic panels are best suited for structures located in regions where year-round air conditioning and small, low-grade, thermal energy demands predominate. Hybrid collectors are to be tested according to ASHRAE standards and a full-system experiment incorporating a photovoltaic array installed at the Solar Energy Research Facility of the University of Texas will be conducted by Lincoln Laboratory.
A microarray gene expression data classification using hybrid back propagation neural network
Directory of Open Access Journals (Sweden)
Vimaladevi M.
2014-01-01
Full Text Available Classification of cancer establishes appropriate treatment and helps to decide the diagnosis. Cancer expands progressively from an alteration in a cell's genetic structure. This change (mutation results in cells with uncontrolled growth patterns. In cancer classification, the approach, Back propagation is sufficient and also it is a universal technique of training artificial neural networks. It is also called supervised learning method. It needs many dataset for input and output for making up the training set. The back propagation method may execute the function of collaborate multiple parties. In existing method, collaborative learning is limited and it considers only two parties. The proposed collaborative function can perform well and problems can be solved by utilizing the power of cloud computing. This technical note applies hybrid models of Back Propagation Neural networks (BPN and fast Genetic Algorithms (GA to estimate the feature selection in gene expression data. The proposed research work examines many feature selection algorithms which are “fragile”; that is, the superiority of their results varies broadly over data sets. By this research, it is suggested that this is due to higherorder interactions between features causing restricted minima in search space in which the algorithm becomes attentive. GAs may escape from such minima by chance, because works are highly stochastic. A neural net classifier with a genetic algorithm, using the GA to select features for classification by the neural net and incorporating the net as part of the objective function of the GA.
Control system for a hybrid powertrain system
Energy Technology Data Exchange (ETDEWEB)
Naqvi, Ali K.; Demirovic, Besim; Gupta, Pinaki; Kaminsky, Lawrence A.
2014-09-09
A vehicle includes a powertrain with an engine, first and second torque machines, and a hybrid transmission. A method for operating the vehicle includes operating the engine in an unfueled state, releasing an off-going clutch which when engaged effects operation of the hybrid transmission in a first continuously variable mode, and applying a friction braking torque to a wheel of the vehicle to compensate for an increase in an output torque of the hybrid transmission resulting from releasing the off-going clutch. Subsequent to releasing the off-going clutch, an oncoming clutch which when engaged effects operation of the hybrid transmission in a second continuously variable mode is synchronized. Subsequent to synchronization of the oncoming clutch, the oncoming clutch is engaged.
Application of hybrid coded genetic algorithm in fuzzy neural network controller
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Presents the fuzzy neural network optimized by hybrid coded genetic algorithm of decimal encoding and bi nary encoding, the searching ability and stability of genetic algorithms enhanced by using binary encoding during the crossover operation and decimal encoding during the mutation operation, and the way of accepting new individuals by probability adopted, by which a new individual is accepted and its parent is discarded when its fitness is higher than that of its parent, and a new individual is accepted by probability when its fitness is lower than that of its parent. And concludes with calculations made with an example that these improvements enhance the speed of genetic algorithms to optimize the fuzzy neural network controller.
Cutting force signal pattern recognition using hybrid neural network in end milling
Institute of Scientific and Technical Information of China (English)
Song-Tae SEONG; Ko-Tae JO; Young-Moon LEE
2009-01-01
Under certain cutting conditions in end milling, the signs of cutting forces change from positive to negative during a revolution of the tool. The change of force direction causes the cutting dynamics to be unstable which results in chatter vibration. Therefore, cutting force signal monitoring and classification are needed to determine the optimal cutting conditions and to improve the efficiency of cut. Artificial neural networks are powerful tools for solving highly complex and nonlinear problems. It can be divided into supervised and unsupervised learning machines based on the availability of a teacher. Hybrid neural network was introduced with both of functions of multilayer perceptron (MLP) trained with the back-propagation algorithm for monitoring and detecting abnormal state, and self organizing feature map (SOFM) for treating huge datum such as image processing and pattern recognition, for predicting and classifying cutting force signal patterns simultaneously. The validity of the results is verified with cutting experiments and simulation tests.
A hybrid Evolutionary Functional Link Artificial Neural Network for Data mining and Classification
Directory of Open Access Journals (Sweden)
Faissal MILI
2012-08-01
Full Text Available This paper presents a specific structure of neural network as the functional link artificial neural network (FLANN. This technique has been employed for classification tasks of data mining. In fact, there are a few studies that used this tool for solving classification problems. In this present research, we propose a hybrid FLANN (HFLANN model, where the optimization process is performed using 3 known population based techniques such as genetic algorithms, particle swarm and differential evolution. This model will be empirically compared to FLANN based back-propagation algorithm and to others classifiers as decision tree, multilayer perceptron based back-propagation algorithm, radical basic function, support vector machine, and K-nearest Neighbor. Our results proved that the proposed model outperforms the other single model. (Abstract
Hybrid singular systems of differential equations
Institute of Scientific and Technical Information of China (English)
殷刚; 张纪峰
2002-01-01
This work develops hybrid models for large-scale singular differential system and analyzestheir asymptotic properties. To take into consideration the discrete shifts in regime across whichthe behavior of the corresponding dynamic systems is markedly different, our goals are to develophybrid systems in which continuous dynamics are intertwined with discrete events under random-jumpdisturbances and to reduce complexity of large-scale singular systems via singularly perturbed Markovchains. To reduce the complexity of large-scale hybrid singular systems, two-time scale is used in theformulation. Under general assumptions, limit behavior of the underlying system is examined. Usingweak convergence methods, it is shown that the systems can be approximated by limit systems inwhich the coefficients are averaged out with respect to the quasi-stationary distributions. Since thelimit systems have fewer states, the complexity is much reduced.
Analysis and design of hybrid control systems
Energy Technology Data Exchange (ETDEWEB)
Malmborg, J.
1998-05-01
Different aspects of hybrid control systems are treated: analysis, simulation, design and implementation. A systematic methodology using extended Lyapunov theory for design of hybrid systems is developed. The methodology is based on conventional control designs in separate regions together with a switching strategy. Dynamics are not well defined if the control design methods lead to fast mode switching. The dynamics depend on the salient features of the implementation of the mode switches. A theorem for the stability of second order switching together with the resulting dynamics is derived. The dynamics on an intersection of two sliding sets are defined for two relays working on different time scales. The current simulation packages have problems modeling and simulating hybrid systems. It is shown how fast mode switches can be found before or during simulation. The necessary analysis work is a very small overhead for a modern simulation tool. To get some experience from practical problems with hybrid control the switching strategy is implemented in two different software environments. In one of them a time-optimal controller is added to an existing PID controller on a commercial control system. Successful experiments with this hybrid controller shows the practical use of the method 78 refs, 51 figs, 2 tabs
Evolved Finite State Controller For Hybrid System
DEFF Research Database (Denmark)
Dupuis, Jean-Francois; Fan, Zhun; Goodman, Erik
2009-01-01
This paper presents an evolutionary methodology to automatically generate nite state automata (FSA) controllers to control hybrid systems. FSA controllers for a case study of two-tank system have been successfully obtained using the proposed evolutionary approach. Experimental results show...
A Fast Hybrid Algorithm of Global Optimization for Feedforward Neural Networks
Institute of Scientific and Technical Information of China (English)
JIANG Minghu; ZHANG Bo; ZHU Xiaoyan; JINAG Mingyan
2001-01-01
This paper presents the hybrid algorithm of global optimization of dynamic learning rate for multilayer feedforward neural networks (MLFNN).The effect of inexact line search on conjugacy was studied, based on which a generalized conjugate gradient method was proposed, showing global convergence for error backpagation of MLFNN. It overcomes the drawback of conventional BP and Polak-Ribieve conjugate gradient algorithms that maybe plunge into local minima. The hybrid algorithm's recognition rate is higher than that of Polak-Ribieve algorithm and convergence BP for test data, its training time is less than that of Fletcher-Reeves algorithm and far less than that of convergence BP, and it has a less complicated and stronger robustness to real speech data.
Hybrid Method for the Navigation of Mobile Robot Using Fuzzy Logic and Spiking Neural Networks
Directory of Open Access Journals (Sweden)
Zineb LAOUICI
2014-11-01
Full Text Available the aim of this paper is to present a strategy describing a hybrid approach for the navigation of a mobile robot in a partially known environment. The main idea is to combine between fuzzy logic approach suitable for the navigation in an unknown environment and spiking neural networks approach for solving the problem of navigation in a known environment. In the literature, many approaches exist for the navigation purpose, for solving separately the problem in both situations. Our idea is based on the fact that we consider a mixed environment, and try to exploit the known environment parts for improving the path and time of navigation between the starting point and the target. The Simulation results, which are shown on two simulated scenarios, indicate that the hybridization improves the performance of robot navigation with regard to path length and the time of navigation.
Neural network controller development for a magnetically suspended flywheel energy storage system
Fittro, Roger L.; Pang, Da-Chen; Anand, Davinder K.
1994-01-01
A neural network controller has been developed to accommodate disturbances and nonlinearities and improve the robustness of a magnetically suspended flywheel energy storage system. The controller is trained using the back propagation-through-time technique incorporated with a time-averaging scheme. The resulting nonlinear neural network controller improves system performance by adapting flywheel stiffness and damping based on operating speed. In addition, a hybrid multi-layered neural network controller is developed off-line which is capable of improving system performance even further. All of the research presented in this paper was implemented via a magnetic bearing computer simulation. However, careful attention was paid to developing a practical methodology which will make future application to the actual bearing system fairly straightforward.
Directory of Open Access Journals (Sweden)
Zhongshan Yang
2016-01-01
Full Text Available Wind speed high-accuracy forecasting, an important part of the electrical system monitoring and control, is of the essence to protect the safety of wind power utilization. However, the wind speed signals are always intermittent and intrinsic complexity; therefore, it is difficult to forecast them accurately. Many traditional wind speed forecasting studies have focused on single models, which leads to poor prediction accuracy. In this paper, a new hybrid model is proposed to overcome the shortcoming of single models by combining singular spectrum analysis, modified intelligent optimization, and the rolling Elman neural network. In this model, except for the multiple seasonal patterns used to reduce interferences from the original data, the rolling model is utilized to forecast the multistep wind speed. To verify the forecasting ability of the proposed hybrid model, 10 min and 60 min wind speed data from the province of Shandong, China, were proposed in this paper as the case study. Compared to the other models, the proposed hybrid model forecasts the wind speed with higher accuracy.
Neurale Netwerken en Radarsystemen (Neural Networks and Radar Systems)
1989-08-01
34 on "godistribucord geheugon" (zie hoofdstuk 5). Neurologiach on psychologisch ondorzoek dienden bij doze vorm van onderzoek alechta ala...momenteel geen goede algoritmen bekend zijn. Een voorbeeld hiervan is het herkennen van typen objecten aon do hand van gebrekkige, onnauvkeurige of gestoorde...valt onder meer to denken aan do betrouwbaarheld van neurale netwerken. Hot is bekond dat sommige typen netwerken nog steeds redelijk good functioneren
Systems Engineering of Electric and Hybrid Vehicles
Kurtz, D. W.; Levin, R. R.
1986-01-01
Technical paper notes systems engineering principles applied to development of electric and hybrid vehicles such that system performance requirements support overall program goal of reduced petroleum consumption. Paper discusses iterative design approach dictated by systems analyses. In addition to obvious peformance parameters of range, acceleration rate, and energy consumption, systems engineering also considers such major factors as cost, safety, reliability, comfort, necessary supporting infrastructure, and availability of materials.
Systems Engineering of Electric and Hybrid Vehicles
Kurtz, D. W.; Levin, R. R.
1986-01-01
Technical paper notes systems engineering principles applied to development of electric and hybrid vehicles such that system performance requirements support overall program goal of reduced petroleum consumption. Paper discusses iterative design approach dictated by systems analyses. In addition to obvious peformance parameters of range, acceleration rate, and energy consumption, systems engineering also considers such major factors as cost, safety, reliability, comfort, necessary supporting infrastructure, and availability of materials.
Hybrid piezoelectric energy harvesting transducer system
Xu, Tian-Bing (Inventor); Jiang, Xiaoning (Inventor); Su, Ji (Inventor); Rehrig, Paul W. (Inventor); Hackenberger, Wesley S. (Inventor)
2008-01-01
A hybrid piezoelectric energy harvesting transducer system includes: (a) first and second symmetric, pre-curved piezoelectric elements mounted separately on a frame so that their concave major surfaces are positioned opposite to each other; and (b) a linear piezoelectric element mounted separately on the frame and positioned between the pre-curved piezoelectric elements. The pre-curved piezoelectric elements and the linear piezoelectric element are spaced from one another and communicate with energy harvesting circuitry having contact points on the frame. The hybrid piezoelectric energy harvesting transducer system has a higher electromechanical energy conversion efficiency than any known piezoelectric transducer.
Hybrid Battery Ultracapacitor System For Human Robotic Systems Project
National Aeronautics and Space Administration — The objective of this proposal is to develop a hybrid battery-ultra capacitor storage system that powers human-robotic systems in space missions. Space missions...
Multiuser hybrid switched-selection diversity systems
Shaqfeh, Mohammad
2011-09-01
A new multiuser scheduling scheme is proposed and analyzed in this paper. The proposed system combines features of conventional full-feedback selection-based diversity systems and reduced-feedback switch-based diversity systems. The new hybrid system provides flexibility in trading-off the channel information feedback overhead with the prospected multiuser diversity gains. The users are clustered into groups, and the users\\' groups are ordered into a sequence. Per-group feedback thresholds are used and optimized to maximize the system overall achievable rate. The proposed hybrid system applies switched diversity criterion to choose one of the groups, and a selection criterion to decide the user to be scheduled from the chosen group. Numerical results demonstrate that the system capacity increases as the number of users per group increases, but at the cost of more required feedback messages. © 2011 IEEE.
A Spiking Neural Learning Classifier System
Howard, Gerard; Lanzi, Pier-Luca
2012-01-01
Learning Classifier Systems (LCS) are population-based reinforcement learners used in a wide variety of applications. This paper presents a LCS where each traditional rule is represented by a spiking neural network, a type of network with dynamic internal state. We employ a constructivist model of growth of both neurons and dendrites that realise flexible learning by evolving structures of sufficient complexity to solve a well-known problem involving continuous, real-valued inputs. Additionally, we extend the system to enable temporal state decomposition. By allowing our LCS to chain together sequences of heterogeneous actions into macro-actions, it is shown to perform optimally in a problem where traditional methods can fail to find a solution in a reasonable amount of time. Our final system is tested on a simulated robotics platform.
Convergent evolution of neural systems in ctenophores.
Moroz, Leonid L
2015-02-15
Neurons are defined as polarized secretory cells specializing in directional propagation of electrical signals leading to the release of extracellular messengers - features that enable them to transmit information, primarily chemical in nature, beyond their immediate neighbors without affecting all intervening cells en route. Multiple origins of neurons and synapses from different classes of ancestral secretory cells might have occurred more than once during ~600 million years of animal evolution with independent events of nervous system centralization from a common bilaterian/cnidarian ancestor without the bona fide central nervous system. Ctenophores, or comb jellies, represent an example of extensive parallel evolution in neural systems. First, recent genome analyses place ctenophores as a sister group to other animals. Second, ctenophores have a smaller complement of pan-animal genes controlling canonical neurogenic, synaptic, muscle and immune systems, and developmental pathways than most other metazoans. However, comb jellies are carnivorous marine animals with a complex neuromuscular organization and sophisticated patterns of behavior. To sustain these functions, they have evolved a number of unique molecular innovations supporting the hypothesis of massive homoplasies in the organization of integrative and locomotory systems. Third, many bilaterian/cnidarian neuron-specific genes and 'classical' neurotransmitter pathways are either absent or, if present, not expressed in ctenophore neurons (e.g. the bilaterian/cnidarian neurotransmitter, γ-amino butyric acid or GABA, is localized in muscles and presumed bilaterian neuron-specific RNA-binding protein Elav is found in non-neuronal cells). Finally, metabolomic and pharmacological data failed to detect either the presence or any physiological action of serotonin, dopamine, noradrenaline, adrenaline, octopamine, acetylcholine or histamine - consistent with the hypothesis that ctenophore neural systems evolved
Time-series analysis with a hybrid Box-Jenkins ARIMA and neural network model
Institute of Scientific and Technical Information of China (English)
Dilli R Aryal; WANG Yao-wu(王要武)
2004-01-01
Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades.More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model's unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.
Wind-solar Hybrid Power System
Jin, Fei
2014-01-01
In the development and utilization of new energy sources, the solar energy and wind energy are paid more attention by various countries, and have become a new field of energy development and utilization of the highest level, the most mature technology, the most widely used and commercial development conditions for new energy. But both the traditional wind power system and solar power system have the characteristic of energy instability. Therefore, wind-solar hybrid power system was proposed i...
Mathematical Modeling of Hybrid Electrical Engineering Systems
Directory of Open Access Journals (Sweden)
A. A. Lobaty
2016-01-01
Full Text Available A large class of systems that have found application in various industries and households, electrified transportation facilities and energy sector has been classified as electrical engineering systems. Their characteristic feature is a combination of continuous and discontinuous modes of operation, which is reflected in the appearance of a relatively new term “hybrid systems”. A wide class of hybrid systems is pulsed DC converters operating in a pulse width modulation, which are non-linear systems with variable structure. Using various methods for linearization it is possible to obtain linear mathematical models that rather accurately simulate behavior of such systems. However, the presence in the mathematical models of exponential nonlinearities creates considerable difficulties in the implementation of digital hardware. The solution can be found while using an approximation of exponential functions by polynomials of the first order, that, however, violates the rigor accordance of the analytical model with characteristics of a real object. There are two practical approaches to synthesize algorithms for control of hybrid systems. The first approach is based on the representation of the whole system by a discrete model which is described by difference equations that makes it possible to synthesize discrete algorithms. The second approach is based on description of the system by differential equations. The equations describe synthesis of continuous algorithms and their further implementation in a digital computer included in the control loop system. The paper considers modeling of a hybrid electrical engineering system using differential equations. Neglecting the pulse duration, it has been proposed to describe behavior of vector components in phase coordinates of the hybrid system by stochastic differential equations containing generally non-linear differentiable random functions. A stochastic vector-matrix equation describing dynamics of the
Hessian matrix estimation in hybrid systems based on an embedded FFNN.
Baek, Seung-Mook; Park, Jung-Wook
2010-10-01
This paper describes the Hessian matrix estimation of nonsmooth nonlinear parameters by the identifier based on a feedforward neural network (FFNN) embedded in a hybrid system, which is modeled by the differential-algebraic-impulsive-switched (DAIS) structure. After identifying full dynamics of the hybrid system, the FFNN is used to estimate second-order derivatives of an objective function J with respect to the nonlinear parameters from the gradient information, which are trajectory sensitivities. Then, the estimated Hessian matrix is applied to the optimal tuning of a saturation limiter used in a practical engineering system.
Detection of cardiovascular anomalies: Hybrid systems approach
Ledezma, Fernando
2012-06-06
In this paper, we propose a hybrid interpretation of the cardiovascular system. Based on a model proposed by Simaan et al. (2009), we study the problem of detecting cardiovascular anomalies that can be caused by variations in some physiological parameters, using an observerbased approach. We present the first numerical results obtained. © 2012 IFAC.
A hybrid deep neural network and physically based distributed model for river stage prediction
hitokoto, Masayuki; sakuraba, Masaaki
2016-04-01
We developed the real-time river stage prediction model, using the hybrid deep neural network and physically based distributed model. As the basic model, 4 layer feed-forward artificial neural network (ANN) was used. As a network training method, the deep learning technique was applied. To optimize the network weight, the stochastic gradient descent method based on the back propagation method was used. As a pre-training method, the denoising autoencoder was used. Input of the ANN model is hourly change of water level and hourly rainfall, output data is water level of downstream station. In general, the desirable input of the ANN has strong correlation with the output. In conceptual hydrological model such as tank model and storage-function model, river discharge is governed by the catchment storage. Therefore, the change of the catchment storage, downstream discharge subtracted from rainfall, can be the potent input candidate of the ANN model instead of rainfall. From this point of view, the hybrid deep neural network and physically based distributed model was developed. The prediction procedure of the hybrid model is as follows; first, downstream discharge was calculated by the distributed model, and then estimates the hourly change of catchment storage form rainfall and calculated discharge as the input of the ANN model, and finally the ANN model was calculated. In the training phase, hourly change of catchment storage can be calculated by the observed rainfall and discharge data. The developed model was applied to the one catchment of the OOYODO River, one of the first-grade river in Japan. The modeled catchment is 695 square km. For the training data, 5 water level gauging station and 14 rain-gauge station in the catchment was used. The training floods, superior 24 events, were selected during the period of 2005-2014. Prediction was made up to 6 hours, and 6 models were developed for each prediction time. To set the proper learning parameters and network
Expert System Based on Data Mining and Neural Networks
Institute of Scientific and Technical Information of China (English)
NI Zhi-wei; JIA Rui-yu
2001-01-01
On the basis of data mining and neural network, this paper proposes a general framework of the neural network expert system and discusses the key techniques in this kind of system. We apply these ideas on agricultural expert system to find some unknown useful knowledge and get some satisfactory results.
Hybrid Energy System Modeling in Modelica
Energy Technology Data Exchange (ETDEWEB)
William R. Binder; Christiaan J. J. Paredis; Humberto E. Garcia
2014-03-01
In this paper, a Hybrid Energy System (HES) configuration is modeled in Modelica. Hybrid Energy Systems (HES) have as their defining characteristic the use of one or more energy inputs, combined with the potential for multiple energy outputs. Compared to traditional energy systems, HES provide additional operational flexibility so that high variability in both energy production and consumption levels can be absorbed more effectively. This is particularly important when including renewable energy sources, whose output levels are inherently variable, determined by nature. The specific HES configuration modeled in this paper include two energy inputs: a nuclear plant, and a series of wind turbines. In addition, the system produces two energy outputs: electricity and synthetic fuel. The models are verified through simulations of the individual components, and the system as a whole. The simulations are performed for a range of component sizes, operating conditions, and control schemes.
An Assessment of a Proposed Hybrid Neural Network for Daily Flow Prediction in Arid Climate
Directory of Open Access Journals (Sweden)
Milad Jajarmizadeh
2014-01-01
Full Text Available Rainfall-runoff simulation in hydrology using artificial intelligence presents the nonlinear relationships using neural networks. In this study, a hybrid network presented as a feedforward modular neural network (FF-MNN has been developed to predict the daily rainfall-runoff of the Roodan watershed at the southern part of Iran. This FF-MNN has three layers—input, hidden, and output. The hidden layer has two types of neural expert or module. Hydrometeorological data of the catchment were collected for 21 years. Heuristic method was used to develop the MNN for exploring daily flow generalization. Two training algorithms, namely, backpropagation with momentum and Levenberg-Marquardt, were used. Sigmoid and linear transfer functions were employed to explore the network’s optimum behavior. Cross-validation and predictive uncertainty assessments were carried out to protect overtiring and overparameterization, respectively. Results showed that the FF-MNN could satisfactorily predict stream flow during testing period. The Nash-Sutcliff coefficient, coefficient of determination, and root mean square error obtained using MNN during training and test periods were 0.85, 0.85, and 39.4 and 0.57, 0.58, and 32.2, respectively. The predictive uncertainties for both periods were 0.39 and 0.44, respectively. Generally, the study showed that the FF-MNN can give promising prediction for rainfall-runoff relations.
Zhou, Qingping; Jiang, Haiyan; Wang, Jianzhou; Zhou, Jianling
2014-10-15
Exposure to high concentrations of fine particulate matter (PM₂.₅) can cause serious health problems because PM₂.₅ contains microscopic solid or liquid droplets that are sufficiently small to be ingested deep into human lungs. Thus, daily prediction of PM₂.₅ levels is notably important for regulatory plans that inform the public and restrict social activities in advance when harmful episodes are foreseen. A hybrid EEMD-GRNN (ensemble empirical mode decomposition-general regression neural network) model based on data preprocessing and analysis is firstly proposed in this paper for one-day-ahead prediction of PM₂.₅ concentrations. The EEMD part is utilized to decompose original PM₂.₅ data into several intrinsic mode functions (IMFs), while the GRNN part is used for the prediction of each IMF. The hybrid EEMD-GRNN model is trained using input variables obtained from principal component regression (PCR) model to remove redundancy. These input variables accurately and succinctly reflect the relationships between PM₂.₅ and both air quality and meteorological data. The model is trained with data from January 1 to November 1, 2013 and is validated with data from November 2 to November 21, 2013 in Xi'an Province, China. The experimental results show that the developed hybrid EEMD-GRNN model outperforms a single GRNN model without EEMD, a multiple linear regression (MLR) model, a PCR model, and a traditional autoregressive integrated moving average (ARIMA) model. The hybrid model with fast and accurate results can be used to develop rapid air quality warning systems.
Compact Hybrid Automotive Propulsion System
Lupo, G.
1986-01-01
Power train proposed for experimental vehicle powered by internal combustion engine and electric motor. Intended for front-wheel drive automobile, power train mass produced using existing technology. System includes internal-combustion engine, electric motor, continuously variable transmission, torque converter, differential, and control and adjustment systems for electric motor and transmission. Continuously variable transmission integrated into hydraulic system that also handles power steering and power brakes. Batteries for electric motor mounted elsewhere in vehicle.
An Artificial Neural Network Control System for Spacecraft Attitude Stabilization
1990-06-01
NAVAL POSTGRADUATE SCHOOL Monterey, California ’-DTIC 0 ELECT f NMARO 5 191 N S, U, THESIS B . AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR...NO. NO. NO ACCESSION NO 11. TITLE (Include Security Classification) AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR SPACECRAFT ATTITUDE STABILIZATION...obsolete a U.S. G v pi.. iim n P.. oiice! toog-eo.5s43 i Approved for public release; distribution is unlimited. AN ARTIFICIAL NEURAL NETWORK CONTROL
Theory of Neural Information Processing Systems
Energy Technology Data Exchange (ETDEWEB)
Galla, Tobias [Abdus Salam International Centre for Theoretical Physics and INFM/CNR SISSA-Unit, Strada Costiera 11, I-34014 Trieste (Italy)
2006-04-07
It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 10{sup 11} neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kuehn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard
Nuclear Hybrid Energy System Model Stability Testing
Energy Technology Data Exchange (ETDEWEB)
Greenwood, Michael Scott [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Cetiner, Sacit M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Fugate, David W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2017-04-01
A Nuclear Hybrid Energy System (NHES) uses a nuclear reactor as the basic power generation unit, and the power generated is used by multiple customers as combinations of thermal power or electrical power. The definition and architecture of a particular NHES can be adapted based on the needs and opportunities of different localities and markets. For example, locations in need of potable water may be best served by coupling a desalination plant to the NHES. Similarly, a location near oil refineries may have a need for emission-free hydrogen production. Using the flexible, multi-domain capabilities of Modelica, Argonne National Laboratory, Idaho National Laboratory, and Oak Ridge National Laboratory are investigating the dynamics (e.g., thermal hydraulics and electrical generation/consumption) and cost of a hybrid system. This paper examines the NHES work underway, emphasizing the control system developed for individual subsystems and the overall supervisory control system.
Hybrid system of semiconductor and photosynthetic protein.
Kim, Younghye; Shin, Seon Ae; Lee, Jaehun; Yang, Ki Dong; Nam, Ki Tae
2014-08-29
Photosynthetic protein has the potential to be a new attractive material for solar energy absorption and conversion. The development of semiconductor/photosynthetic protein hybrids is an example of recent progress toward efficient, clean and nanostructured photoelectric systems. In the review, two biohybrid systems interacting through different communicating methods are addressed: (1) a photosynthetic protein immobilized semiconductor electrode operating via electron transfer and (2) a hybrid of semiconductor quantum dots and photosynthetic protein operating via energy transfer. The proper selection of materials and functional and structural modification of the components and optimal conjugation between them are the main issues discussed in the review. In conclusion, we propose the direction of future biohybrid systems for solar energy conversion systems, optical biosensors and photoelectric devices.
Analysis of hybrid solar systems
Swisher, J.
1980-10-01
The TRNSYS simulation program was used to evaluate the performance of active charge/passive discharge solar systems with water as the working fluid. TRNSYS simulations are used to evaluate the heating performance and cooling augmentation provided by systems in several climates. The results of the simulations are used to develop a simplified analysis tool similar to the F-chart and Phi-bar procedures used for active systems. This tool, currently in a preliminary stage, should provide the designer with quantitative performance estimates for comparison with other passive, active, and nonsolar heating and cooling designs.
2007-05-02
IEEE Transactions on Automatic Control , vol. 48, no. 1, pp. 2-17, January 2003. [4] A. Megretski, "Robustness...34Output stabilizability of discrete-event dynamic systems", IEEE Transactions on Automatic Control , vol. 36, no.8, pp. 925-935, August 1991. [6] K...Passino, A. Michel and P. Antsaklis, "Lyapunov stability of a class of discrete event systems", IEEE Transactions on Automatic Control , vol. 39, no.
Optimised Hybrid Integrated Renewable Energy System
Directory of Open Access Journals (Sweden)
Dr. Arun Sandilya
2012-10-01
Full Text Available A hybrid integrated renewable energy system for an isolated small community, where grid extension is considered uneconomical. This paper proposed cost optimization through dynamic matching between load and proper equipment sizing. The Matlab based computer program developed for determining the most cost effective energy source to supply required load any given time of the day. Integrated system based on green energy utilization and rural electricity development.
Hybrid Recommender System based on Autoencoders
Strub, Florian; Gaudel, Romaric; Mary, Jérémie
2016-01-01
International audience; A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings. In the last decades, few attempts where done to handle that objective with Neural Networks, but recently an architecture based on Autoencoders proved to be a promising approach. In current paper, we enhanced that architecture (i) by using a loss function adapted to input data with missing...
Analysis of Synchronization for Coupled Hybrid Systems
DEFF Research Database (Denmark)
Li, Zheng; Wisniewski, Rafal
2006-01-01
In the control systems with coupled multi-subsystem, the subsystems might be synchronized (i.e. all the subsystems have the same operation states), which results in negative influence to the whole system. For example, in the supermarket refrigeration systems, the synchronized switch of each...... subsystem will cause low efficiency, inferior control performance and a high wear on the compressor. This paper takes the supermarket refrigeration systems as an example to analyze the synchronization and its coupling strengths of coupled hybrid systems, which may provide a base for further research...
Artificial Neural Network System for Thyroid Diagnosis
Directory of Open Access Journals (Sweden)
Mazin Abdulrasool Hameed
2017-05-01
Full Text Available Thyroid disease is one of major causes of severe medical problems for human beings. Therefore, proper diagnosis of thyroid disease is considered as an important issue to determine treatment for patients. This paper focuses on using Artificial Neural Network (ANN as a significant technique of artificial intelligence to diagnose thyroid diseases. The continuous values of three laboratory blood tests are used as input signals to the proposed system of ANN. All types of thyroid diseases that may occur in patients are taken into account in design of system, as well as the high accuracy of the detection and categorization of thyroid diseases are considered in the system. A multilayer feedforward architecture of ANN is adopted in the proposed design, and the back propagation is selected as learning algorithm to accomplish the training process. The result of this research shows that the proposed ANN system is able to precisely diagnose thyroid disease, and can be exploited in practical uses. The system is simulated via MATLAB software to evaluate its performance
Biomolecule/nanomaterial hybrid systems for nanobiotechnology.
Tel-Vered, Ran; Yehezkeli, Omer; Willner, Itamar
2012-01-01
The integration of biomolecules with metallic or semiconductor nanoparticles or carbon nanotubes yields new hybrid nanostructures of unique features that combine the properties of the biomolecules and of the nano-elements. These unique features of the hybrid biomolecule/nanoparticle systems provide the basis for the rapid development of the area of nanobiotechnology. Recent advances in the implementation of hybrid materials consisting of biomolecules and metallic nanoparticles or semiconductor quantum dots will be discussed. The following topics will be exemplified: (i) The electrical wiring of redox enzymes with electrodes by means of metallic nanoparticles or carbon nanotubes, and the application of the modified electrodes as amperometric biosensors or for the construction of biofuel cells. (ii) The biocatalytic growth of metallic nanoparticles as a means to construct optical or electrical sensors. (iii) The functionalization of semiconductor quantum dots with biomolecules and the application of the hybrid nanostructures for developing different optical sensors, including intracellular sensor systems. (iv) The use of biomolecule-metallic nanoparticle nanostructures as templates for growing metallic nanowires, and the construction of fuel-driven nano-transporters.
Disease processes as hybrid dynamical systems
Directory of Open Access Journals (Sweden)
Pietro Liò
2012-08-01
Full Text Available We investigate the use of hybrid techniques in complex processes of infectious diseases. Since predictive disease models in biomedicine require a multiscale approach for understanding the molecule-cell-tissue-organ-body interactions, heterogeneous methodologies are often employed for describing the different biological scales. Hybrid models provide effective means for complex disease modelling where the action and dosage of a drug or a therapy could be meaningfully investigated: the infection dynamics can be classically described in a continuous fashion, while the scheduling of multiple treatment discretely. We define an algebraic language for specifying general disease processes and multiple treatments, from which a semantics in terms of hybrid dynamical system can be derived. Then, the application of control-theoretic tools is proposed in order to compute the optimal scheduling of multiple therapies. The potentialities of our approach are shown in the case study of the SIR epidemic model and we discuss its applicability on osteomyelitis, a bacterial infection affecting the bone remodelling system in a specific and multiscale manner. We report that formal languages are helpful in giving a general homogeneous formulation for the different scales involved in a multiscale disease process; and that the combination of hybrid modelling and control theory provides solid grounds for computational medicine.
Neural Network for Optimization of Existing Control Systems
DEFF Research Database (Denmark)
Madsen, Per Printz
1995-01-01
The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....
Universal blind quantum computation for hybrid system
Huang, He-Liang; Bao, Wan-Su; Li, Tan; Li, Feng-Guang; Fu, Xiang-Qun; Zhang, Shuo; Zhang, Hai-Long; Wang, Xiang
2017-08-01
As progress on the development of building quantum computer continues to advance, first-generation practical quantum computers will be available for ordinary users in the cloud style similar to IBM's Quantum Experience nowadays. Clients can remotely access the quantum servers using some simple devices. In such a situation, it is of prime importance to keep the security of the client's information. Blind quantum computation protocols enable a client with limited quantum technology to delegate her quantum computation to a quantum server without leaking any privacy. To date, blind quantum computation has been considered only for an individual quantum system. However, practical universal quantum computer is likely to be a hybrid system. Here, we take the first step to construct a framework of blind quantum computation for the hybrid system, which provides a more feasible way for scalable blind quantum computation.
ANN Approach for State Estimation of Hybrid Systems and Its Experimental Validation
Directory of Open Access Journals (Sweden)
Shijoh Vellayikot
2015-01-01
Full Text Available A novel artificial neural network based state estimator has been proposed to ensure the robustness in the state estimation of autonomous switching hybrid systems under various uncertainties. Taking the autonomous switching three-tank system as benchmark hybrid model working under various additive and multiplicative uncertainties such as process noise, measurement error, process–model parameter variation, initial state mismatch, and hand valve faults, real-time performance evaluation by the comparison of it with other state estimators such as extended Kalman filter and unscented Kalman Filter was carried out. The experimental results reported with the proposed approach show considerable improvement in the robustness in performance under the considered uncertainties.
Hybrid digital signal processing and neural networks for automated diagnostics using NDE methods
Energy Technology Data Exchange (ETDEWEB)
Upadhyaya, B.R.; Yan, W. [Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
1993-11-01
The primary purpose of the current research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive examination data. Specifically, data from eddy current inspection of heat exchanger tubing were utilized to evaluate this technology. The focus of the research was to develop and test various data compression methods (for eddy current data) and the performance of different neural network paradigms for defect classification and defect parameter estimation. Feedforward, fully-connected neural networks, that use the back-propagation algorithm for network training, were implemented for defect classification and defect parameter estimation using a modular network architecture. A large eddy current tube inspection database was acquired from the Metals and Ceramics Division of ORNL. These data were used to study the performance of artificial neural networks for defect type classification and for estimating defect parameters. A PC-based data preprocessing and display program was also developed as part of an expert system for data management and decision making. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods. The integration of data compression and artificial neural networks for information processing was established as an effective technique for automation of diagnostics using nondestructive examination methods.
Hybrid compensation arrangement in dispersed generation systems
DEFF Research Database (Denmark)
Chen, Zhe; Blaabjerg, Frede; Pedersen, John Kim
2005-01-01
This paper presents a hybrid compensation system consisting of an active filter and distributed passive filters. In the system, each individual passive filter is connected to a distortion source and designed to eliminate main harmonics and supply reactive power for the distortion source, while...... are performed for a power system including the dispersed generation units connected into the system through power electronic converters and diode rectifier loads, which produce the distorted waveforms. The simulation results have demonstrated that good compensation effects can be achieved by using the combined...
Hybrid ventilation systems and high performance buildings
Energy Technology Data Exchange (ETDEWEB)
Utzinger, D.M. [Wisconsin Univ., Milwaukee, WI (United States). School of Architecture and Urban Planning
2009-07-01
This paper described hybrid ventilation design strategies and their impact on 3 high performance buildings located in southern Wisconsin. The Hybrid ventilation systems combined occupant controlled natural ventilation with mechanical ventilation systems. Natural ventilation was shown to provide adequate ventilation when appropriately designed. Proper control integration of natural ventilation into hybrid systems was shown to reduce energy consumption in high performance buildings. This paper also described the lessons learned from the 3 buildings. The author served as energy consultant on all three projects and had the responsibility of designing and integrating the natural ventilation systems into the HVAC control strategy. A post occupancy evaluation of building energy performance has provided learning material for architecture students. The 3 buildings included the Schlitz Audubon Nature Center completed in 2003; the Urban Ecology Center completed in 2004; and the Aldo Leopold Legacy Center completed in 2007. This paper included the size, measured energy utilization intensity and percentage of energy supplied by renewable solar power and bio-fuels on site for each building. 6 refs., 2 tabs., 6 figs.
Viewing hybrid systems as products of control systems and automata
Grossman, R. L.; Larson, R. G.
1992-01-01
The purpose of this note is to show how hybrid systems may be modeled as products of nonlinear control systems and finite state automata. By a hybrid system, we mean a network of consisting of continuous, nonlinear control system connected to discrete, finite state automata. Our point of view is that the automata switches between the control systems, and that this switching is a function of the discrete input symbols or letters that it receives. We show how a nonlinear control system may be viewed as a pair consisting of a bialgebra of operators coding the dynamics, and an algebra of observations coding the state space. We also show that a finite automata has a similar representation. A hybrid system is then modeled by taking suitable products of the bialgebras coding the dynamics and the observation algebras coding the state spaces.
Directory of Open Access Journals (Sweden)
Lukas Falat
2014-01-01
Full Text Available In this paper, authors apply feed-forward artificial neural network (ANN of RBF type into the process of modelling and forecasting the future value of USD/CAD time series. Authors test the customized version of the RBF and add the evolutionary approach into it. They also combine the standard algorithm for adapting weights in neural network with an unsupervised clustering algorithm called K-means. Finally, authors suggest the new hybrid model as a combination of a standard ANN and a moving average for error modeling that is used to enhance the outputs of the network using the error part of the original RBF. Using high-frequency data, they examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, authors perform the comparative out-of-sample analysis of the suggested hybrid model with statistical models and the standard neural network.
Directory of Open Access Journals (Sweden)
Lihua Yang
2015-04-01
Full Text Available Export volume forecasting of fresh fruits is a complex task due to the large number of factors affecting the demand. In order to guide the fruit growers’ sales, decreasing the cultivating cost and increasing their incomes, a hybrid fresh apple export volume forecasting model is proposed. Using the actual data of fresh apple export volume, the Seasonal Decomposition (SD model of time series and Radial Basis Function (RBF model of artificial neural network are built. The predictive results are compared among the three forecasting model based on the criterion of Mean Absolute Percentage Error (MAPE. The result indicates that the proposed combined forecasting model is effective because it can improve the prediction accuracy of fresh apple export volumes.
Directory of Open Access Journals (Sweden)
Abdelkrim Moussaoui
2006-01-01
Full Text Available The authors discuss the combination of an Artificial Neural Network (ANN with analytical models to improve the performance of the prediction model of finishing rolling force in hot strip rolling mill process. The suggested model was implemented using Bayesian Evidence based training algorithm. It was found that the Bayesian Evidence based approach provided a superior and smoother fit to the real rolling mill data. Completely independent set of real rolling data were used to evaluate the capacity of the fitted ANN model to predict the unseen regions of data. As a result, test rolls obtained by the suggested hybrid model have shown high prediction quality comparatively to the usual empirical prediction models.
Institute of Scientific and Technical Information of China (English)
LI Xiang; LIU Guang-ying; QI Jian-xun
2007-01-01
To evaluate the credit risk of customers in power market precisely, the new chaotic searching and fuzzy neural network (FNN)hybrid algorithm were proposed. By combining with the chaotic searching,the learning ability of the FNN was markedly enhanced. Customers'actual credit flaw data of power supply enterprises were collected to carry on the real evaluation, which can be treated as example for the model. The result shows that the proposed method surpasses the traditional statistical models in regard to the precision of forecasting and has a practical value. Compared with the results of ordinary FNN and ANN. the precision of the proposed algorithm call be enhanced by 2.2% and 4.5%. respectively.
Future hybrid systems: solar and hydrogen
Energy Technology Data Exchange (ETDEWEB)
Kazmerski, L.L. [National Renewable Energy Lab., Golden, CO (United States); Broussard, K. [National Renewable Energy Lab., Golden, CO (United States)]|[NREL MURA Intern from Southern Univ., Baton Rouge, LA (United States)
2003-07-01
Future solar and hydrogen hybrid systems are discussed in terms of the evolving hydrogen economy. The focus is on distributed hydrogen, relying on the same distributed-energy strengths of solar-photovoltaic electricity in the built environment. Solar-hydrogen residences, as well as solar parks, are presented. Landarea issues are evaluated, and the economics and potential of these approaches are examined in terms of roadmap predictions on PV and hydrogen pathways. (orig.)
Spectral Selectivity Applied To Hybrid Concentration Systems
Hamdy, M. A.; Luttmann, F.; Osborn, D. E.; Jacobson, M. R.; MacLeod, H. A.
1985-12-01
The efficiency of conversion of concentrated solar energy can be improved by separating the solar spectrum into portions matched to specific photoquantum processes and the balance used for photothermal conversion. The basic approaches of spectrally selective beam splitters are presented. A detailed simulation analysis using TRNSYS is developed for a spectrally selective hybrid photovoltaic/photothermal concentrating system. The analysis shows definite benefits to a spectrally selective approach.
HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS
Directory of Open Access Journals (Sweden)
Giane STUART
1997-12-01
Full Text Available This work presents the results of a Hybrid Neural Network (HNN technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.Neste trabalho são apresentados os resultados obtidos na modelagem da extração supercrítica de óleo essencial de alfavaca e alecrim usando uma rede híbrida neuronal. Utilizou-se uma rede híbrida na configuração em série para estimar os parâmetros do modelo fenomenológico empregado para descrever o processo de extração, o modelo de Sovová. Um pequeno conjunto de dados experimentais, para cada matriz vegetal, foi usado para gerar um conjunto estendido de dados, suficiente para a etapa de treinamento da rede. A validação da presente proposta foi efetuada através da comparação entre os resultados preditos e aqueles obtidos experimentalmente que não constaram do processo de treinamento da rede. Demonstra-se que a rede híbrida neuronal correlaciona e prediz satisfatoriamente os dados experimentais, mostrando-se portanto promissora no campo da modelagem do processo de extração supercrítica.
A HYBRID GENETIC ALGORITHM-NEURAL NETWORK APPROACH FOR PRICING CORES AND REMANUFACTURED CORES
Directory of Open Access Journals (Sweden)
M. Seidi
2012-01-01
Full Text Available
ENGLISH ABSTRACT:Sustainability has become a major issue in most economies, causing many leading companies to focus on product recovery and reverse logistics. Remanufacturing is an industrial process that makes used products reusable. One of the important aspects in both reverse logistics and remanufacturing is the pricing of returned and remanufactured products (called cores. In this paper, we focus on pricing the cores and remanufactured cores. First we present a mathematical model for this purpose. Since this model does not satisfy our requirements, we propose a simulation optimisation approach. This approach consists of a hybrid genetic algorithm based on a neural network employed as the fitness function. We use automata learning theory to obtain the learning rate required for training the neural network. Numerical results demonstrate that the optimal value of the acquisition price of cores and price of remanufactured cores is obtained by this approach.
AFRIKAANSE OPSOMMING: Volhoubaarheid het ‘n belangrike saak geword in die meeste ekonomieë, wat verskeie maatskappye genoop het om produkherwinning en omgekeerde logistiek te onder oë te neem. Hervervaardiging is ‘n industriële proses wat gebruikte produkte weer bruikbaar maak. Een van die belangrike aspekte in beide omgekeerde logistiek en hervervaardiging is die prysbepaling van herwinne en hervervaardigde produkte. Hierdie artikel fokus op die prysbepalingsaspekte by wyse van ‘n wiskundige model.
Hybrid Metaheuristic-Neural Assessment of the Adhesion in Existing Cement Composites
Directory of Open Access Journals (Sweden)
Łukasz Sadowski
2017-04-01
Full Text Available The article presents the hybrid metaheuristic-neural assessment of the pull-off adhesion in existing multi-layer cement composites using artificial neural networks (ANNs and the imperialist competitive algorithm (ICA. The ICA is a metaheuristic algorithm inspired by the human political-social evolution. This method is based solely on the use of ANNs and two non-destructive testing (NDT methods: the impact-echo method (I-E and the impulse response method (IR. In this research, the ICA has been used to optimize the weights of the ANN. The combined ICA-ANN model has been compared to the genetic algorithm (GA and particle swarm optimization (PSO to evaluate its accuracy. The results showed that the ICA-ANN model outperforms other techniques when testing datasets in terms of both effectiveness and efficiency. As presented in the validation stage, it is possible to reliably map the adhesion level on a tested surface without local damage to the latter.
A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks
Directory of Open Access Journals (Sweden)
Nenad Kojić
2012-06-01
Full Text Available The networking infrastructure of wireless mesh networks (WMNs is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs. This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission. The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.
A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks
Kojić, Nenad; Reljin, Irini; Reljin, Branimir
2012-01-01
The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance. PMID:22969360
Arabasadi, Zeinab; Alizadehsani, Roohallah; Roshanzamir, Mohamad; Moosaei, Hossein; Yarifard, Ali Asghar
2017-04-01
Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the proposed method is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network. Making use of such methodology, we achieved accuracy, sensitivity and specificity rates of 93.85%, 97% and 92% respectively, on Z-Alizadeh Sani dataset.
Hybrid Clustering-GWO-NARX neural network technique in predicting stock price
Das, Debashish; Safa Sadiq, Ali; Mirjalili, Seyedali; Noraziah, A.
2017-09-01
Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.
A neural networks-based hybrid routing protocol for wireless mesh networks.
Kojić, Nenad; Reljin, Irini; Reljin, Branimir
2012-01-01
The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic-i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.
A hybrid neural network structure for application to nondestructive TRU waste assay
Energy Technology Data Exchange (ETDEWEB)
Becker, G. [Idaho National Engineering Lab., Idaho Falls, ID (United States)
1995-12-31
The determination of transuranic (TRU) and associated radioactive material quantities entrained in waste forms is a necessary component. of waste characterization. Measurement performance requirements are specified in the National TRU Waste Characterization Program quality assurance plan for which compliance must be demonstrated prior to the transportation and disposition of wastes. With respect to this criterion, the existing TRU nondestructive waste assay (NDA) capability is inadequate for a significant fraction of the US Department of Energy (DOE) complex waste inventory. This is a result of the general application of safeguard-type measurement and calibration schemes to waste form configurations. Incompatibilities between such measurement methods and actual waste form configurations complicate regulation compliance demonstration processes and illustrate the need for an alternate measurement interpretation paradigm. Hence, it appears necessary to supplement or perhaps restructure the perceived solution and approach to the waste NDA problem. The first step is to understand the magnitude of the waste matrix/source attribute space associated with those waste form configurations in inventory and how this creates complexities and unknowns with respect to existing NDA methods. Once defined and/or bounded, a conceptual method must be developed that specifies the necessary tools and the framework in which the tools are used. A promising framework is a hybridized neural network structure. Discussed are some typical complications associated with conventional waste NDA techniques and how improvements can be obtained through the application of neural networks.
Hybrid2: The hybrid system simulation model, Version 1.0, user manual
Energy Technology Data Exchange (ETDEWEB)
Baring-Gould, E.I.
1996-06-01
In light of the large scale desire for energy in remote communities, especially in the developing world, the need for a detailed long term performance prediction model for hybrid power systems was seen. To meet these ends, engineers from the National Renewable Energy Laboratory (NREL) and the University of Massachusetts (UMass) have spent the last three years developing the Hybrid2 software. The Hybrid2 code provides a means to conduct long term, detailed simulations of the performance of a large array of hybrid power systems. This work acts as an introduction and users manual to the Hybrid2 software. The manual describes the Hybrid2 code, what is included with the software and instructs the user on the structure of the code. The manual also describes some of the major features of the Hybrid2 code as well as how to create projects and run hybrid system simulations. The Hybrid2 code test program is also discussed. Although every attempt has been made to make the Hybrid2 code easy to understand and use, this manual will allow many organizations to consider the long term advantages of using hybrid power systems instead of conventional petroleum based systems for remote power generation.
Role of neural network models for developing speech systems
Indian Academy of Sciences (India)
K Sreenivasa Rao
2011-10-01
This paper discusses the application of neural networks for developing different speech systems. Prosodic parameters of speech at syllable level depend on positional, contextual and phonological features of the syllables. In this paper, neural networks are explored to model the prosodic parameters of the syllables from their positional, contextual and phonological features. The prosodic parameters considered in this work are duration and sequence of pitch $(F_0)$ values of the syllables. These prosody models are further examined for applications such as text to speech synthesis, speech recognition, speaker recognition and language identiﬁcation. Neural network models in voice conversion system are explored for capturing the mapping functions between source and target speakers at source, system and prosodic levels. We have also used neural network models for characterizing the emotions present in speech. For identiﬁcation of dialects in Hindi, neural network models are used to capture the dialect speciﬁc information from spectral and prosodic features of speech.
Neural Systems for Speech and Song in Autism
Lai, Grace; Pantazatos, Spiro P.; Schneider, Harry; Hirsch, Joy
2012-01-01
Despite language disabilities in autism, music abilities are frequently preserved. Paradoxically, brain regions associated with these functions typically overlap, enabling investigation of neural organization supporting speech and song in autism. Neural systems sensitive to speech and song were compared in low-functioning autistic and age-matched…
Formalism for the Neural Network of Visual Systems
Stavenga, D.G.; Beersma, D.G.M.
1975-01-01
A formalism to describe neural interrelations is developed on the exemplary case of the fly visual system. Absolute and relative indices are employed to identify the position of neural elements within the lattices of the visual ganglia. Illustrative applications as the projection of fly retinula cel
Advanced propulsion system for hybrid vehicles
Norrup, L. V.; Lintz, A. T.
1980-01-01
A number of hybrid propulsion systems were evaluated for application in several different vehicle sizes. A conceptual design was prepared for the most promising configuration. Various system configurations were parametrically evaluated and compared, design tradeoffs performed, and a conceptual design produced. Fifteen vehicle/propulsion systems concepts were parametrically evaluated to select two systems and one vehicle for detailed design tradeoff studies. A single hybrid propulsion system concept and vehicle (five passenger family sedan)were selected for optimization based on the results of the tradeoff studies. The final propulsion system consists of a 65 kW spark-ignition heat engine, a mechanical continuously variable traction transmission, a 20 kW permanent magnet axial-gap traction motor, a variable frequency inverter, a 386 kg lead-acid improved state-of-the-art battery, and a transaxle. The system was configured with a parallel power path between the heat engine and battery. It has two automatic operational modes: electric mode and heat engine mode. Power is always shared between the heat engine and battery during acceleration periods. In both modes, regenerative braking energy is absorbed by the battery.
System Identification of X-33 Neural Network
Aggarwal, Shiv
2003-01-01
present attempt, as a start, focuses only on the entry phase. Since the main engine remains cut off in this phase, there is no thrust acting on the system. This considerably simplifies the equations of motion. We introduce another simplification by assuming the system to be linear after some non-linearities are removed analytically from our consideration. Under these assumptions, the problem could be solved by Classical Statistics by employing the least sum of squares approach. Instead we chose to use the Neural Network method. This method has many advantages. It is modern, more efficient, can be adapted to work even when the assumptions are diluted. In fact, Neural Networks try to model the human brain and are capable of pattern recognition.
Directory of Open Access Journals (Sweden)
Jamal Salahaldeen Majeed Alneamy
2014-01-01
Full Text Available Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO algorithm and fuzzy wavelet neural network (FWNN for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI machine learning repository. The performance of the proposed method (TLBO_FWNN is estimated using K-fold cross validation based on mean square error (MSE, classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.
A general purpose characterization system for rooftop hybrid microconcentrators
Middleton, Robert; Jones, Christopher; Thomsen, Elizabeth; Diez, Vicente Munoz; Harvey, J.; Everett, Vernie; Blakers, Andrew
2014-09-01
A versatile characterization system for hybrid thermal and photovoltaic solar receivers is presented and demonstrated. The characterization of the thermal loss and effective area of a novel hybrid receiver is presented.
Cultural Change, the Hybrid Administrative System and Public ...
African Journals Online (AJOL)
2013-01-17
Jan 17, 2013 ... crackdown on corruption, this article explores the view that it is the hybrid .... decentralization and public participation in governance creates new demands. ..... The Hybrid Administrative System and Corporate Culture.
Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.
Zhang, Yanjun; Tao, Gang; Chen, Mou
2016-09-01
This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.
Hybrid modeling and prediction of dynamical systems
Lloyd, Alun L.; Flores, Kevin B.
2017-01-01
Scientific analysis often relies on the ability to make accurate predictions of a system’s dynamics. Mechanistic models, parameterized by a number of unknown parameters, are often used for this purpose. Accurate estimation of the model state and parameters prior to prediction is necessary, but may be complicated by issues such as noisy data and uncertainty in parameters and initial conditions. At the other end of the spectrum exist nonparametric methods, which rely solely on data to build their predictions. While these nonparametric methods do not require a model of the system, their performance is strongly influenced by the amount and noisiness of the data. In this article, we consider a hybrid approach to modeling and prediction which merges recent advancements in nonparametric analysis with standard parametric methods. The general idea is to replace a subset of a mechanistic model’s equations with their corresponding nonparametric representations, resulting in a hybrid modeling and prediction scheme. Overall, we find that this hybrid approach allows for more robust parameter estimation and improved short-term prediction in situations where there is a large uncertainty in model parameters. We demonstrate these advantages in the classical Lorenz-63 chaotic system and in networks of Hindmarsh-Rose neurons before application to experimentally collected structured population data. PMID:28692642
Identification and estimation algorithm for stochastic neural system.
Nakao, M; Hara, K; Kimura, M; Sato, R
1984-01-01
An algorithm for the estimation of stochastic processes in a neural system is presented. This process is defined here as the continuous stochastic process reflecting the dynamics of the neural system which has some inputs and generates output spike trains. The algorithm proposed here is to identify the system parameters and then estimate the stochastic process called neural system process here. These procedures carried out on the basis of the output spike trains which are supposed to be the data observed in the randomly missing way by the threshold time function in the neural system. The algorithm is constructed with the well-known Kalman filters and realizes the estimation of the neural system process by cooperating with the algorithm for the parameter estimation of the threshold time function presented previously (Nakao et al., 1983). The performance of the algorithm is examined by applying it to the various spike trains simulated by some artificial models and also to the neural spike trains recorded in cat's optic tract fibers. The results in these applications are thought to prove the effectiveness of the algorithm proposed here to some extent. Such attempts, we think, will serve to improve the characterizing and modelling techniques of the stochastic neural systems.
Differential dynamic logics - automated theorem proving for hybrid systems
Platzer, André
2008-01-01
Hybrid systems are models for complex physical systems and are defined as dynamical systems with interacting discrete transitions and continuous evolutions along differential equations. With the goal of developing a theoretical and practical foundation for deductive verification of hybrid systems, we introduce differential dynamic logic as a new logic with which correctness properties of hybrid systems with parameterized system dynamics can be specified and verified naturally. As a verificati...
Advanced hybrid vehicle propulsion system study
Schwarz, R.
1982-01-01
Results are presented of a study of an advanced heat engine/electric automotive hybrid propulsion system. The system uses a rotary stratified charge engine and ac motor/controller in a parallel hybrid configuration. The three tasks of the study were (1) parametric studies involving five different vehicle types, (2) design trade-off studies to determine the influence of various vehicle and propulsion system paramaters on system performance fuel economy and cost, and (3) a conceptual design establishing feasibility at the selected approach. Energy consumption for the selected system was .034 1/km (61.3 mpg) for the heat engine and .221 kWh/km (.356 kWh/mi) for the electric power system over a modified J227 a schedule D driving cycle. Life cycle costs were 7.13 cents/km (11.5 cents/mi) at $2/gal gasoline and 7 cents/kWh electricity for 160,000 km (100,000 mi) life.
A Dynamical Simulation Facility for Hybrid Systems
Back, A; Myers, M; Back, Allen; Guckenheimer, John; Myers, Mark
1993-01-01
Abstract: This paper establishes a general framework for describing hybrid dynamical systems which is particularly suitable for numerical simulation. In this context, the data structures used to describe the sets and functions which comprise the dynamical system are crucial since they provide the link between a natural mathematical formulation of a problem and the correct application of standard numerical algorithms. We describe a partial implementation of the design methodology and use this simulation tool for a specific control problem in robotics as an illustration of the utility of the approach for practical applications.
Hybrid electric vehicle power management system
Energy Technology Data Exchange (ETDEWEB)
Bissontz, Jay E.
2015-08-25
Level voltage levels/states of charge are maintained among a plurality of high voltage DC electrical storage devices/traction battery packs that are arrayed in series to support operation of a hybrid electric vehicle drive train. Each high voltage DC electrical storage device supports a high voltage power bus, to which at least one controllable load is connected, and at least a first lower voltage level electrical distribution system. The rate of power transfer from the high voltage DC electrical storage devices to the at least first lower voltage electrical distribution system is controlled by DC-DC converters.
Hybrid systems, optimal control and hybrid vehicles theory, methods and applications
Böhme, Thomas J
2017-01-01
This book assembles new methods showing the automotive engineer for the first time how hybrid vehicle configurations can be modeled as systems with discrete and continuous controls. These hybrid systems describe naturally and compactly the networks of embedded systems which use elements such as integrators, hysteresis, state-machines and logical rules to describe the evolution of continuous and discrete dynamics and arise inevitably when modeling hybrid electric vehicles. They can throw light on systems which may otherwise be too complex or recondite. Hybrid Systems, Optimal Control and Hybrid Vehicles shows the reader how to formulate and solve control problems which satisfy multiple objectives which may be arbitrary and complex with contradictory influences on fuel consumption, emissions and drivability. The text introduces industrial engineers, postgraduates and researchers to the theory of hybrid optimal control problems. A series of novel algorithmic developments provides tools for solving engineering pr...
Research of IDSS Architecture Based on Hybrid Systems
Institute of Scientific and Technical Information of China (English)
MA Biao; YANG Bao-an
2005-01-01
This paper discusses the necessity of building IDSS on hybrid systems, and adopts XML technology to manage isomeric knowledge in hybrid systems. The paper proposes a new architecture of hybrid systems based IDSS whose core system is isomeric knowledge system. The architecture is composed of knowledge component, problems processing system, data component and intelligent user interface. This new architecture aims to enhance the capability of integrating hybrid systems, to improve the supporting effectiveness of decision-making and the intelligent level of IDSS, and tries a new way to elevate the system's ability of handling and learning knowledge.
Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.; Birch, Phil M.
2009-04-01
θThe window unit in the design of the complex logarithmic r-θ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-θ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-θ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation.
Directory of Open Access Journals (Sweden)
Bravo S.
2004-01-01
Full Text Available A hybrid neural network model for simulating the process of enzymatic reduction of fructose to sorbitol process catalyzed by glucose-fructose oxidoreductase in Zymomonas mobilis CP4 is presented. Data used to derive and validate the model was obtained from experiments carried out under different conditions of pH, temperature and concentrations of both substrates (glucose and fructose involved in the reaction. Sonicated and lyophilized cells were used as source of the enzyme. The optimal pH for sorbitol synthesis at 30º C is 6.5. For a value of pH of 6, the optimal temperature is 35º C. The neural network in the model computes the value of the kinetic relationship. The hybrid neural network model is able to simulate changes in the substrates and product concentrations during sorbitol synthesis under pH and temperature conditions ranging between 5 and 7.5 and 25 and 40º C, respectively. Under these conditions the rate of sorbitol synthesis shows important differences. Values computed using the hybrid neural network model have an average error of 1.7·10-3 mole.
Verification and Validation of Neural Networks for Aerospace Systems
Mackall, Dale; Nelson, Stacy; Schumann, Johann
2002-01-01
The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: Overview of Adaptive Systems and V&V Processes/Methods.
Utilization of hybrid systems in Cuba
Energy Technology Data Exchange (ETDEWEB)
Gonzalez, Mercedes Menendez; Figueredo, Conrado Moreno [Renewable Energy Technologies Study Center (CETER), Marianao (Cuba)
1996-12-31
This work deals with the possibility of the wind-photovoltaic hybrid system uses for the electricity generation in Cuba. A design of energy installation is made to satisfy the tourism facilities demands located in Cayo Sabinal, in the north of the province Camaguey. The design is based on the data base of the available wind and solar resources. A group of existing wind-generators in the market is analyzed and the best is selected taking into account a set of energy parameters, the monthly energy supply is function of the turbines numbers and the quantity of necessary solar energy to guarantee the system requirements. An economical evaluation is carried out in order to select the best wind-solar combination and a comparison with other forms of electricity generation (Diesel Plant and a stand alone wind system). In the work is showed the best combination in the critical month is when a 62% of energy is supplied by wind energy and 38% of solar energy. Otherwise in the work is showed hybrid system is more economical than a stand alone wind system and a Diesel Plant. (Author)
Dissipative dynamics of superconducting hybrid qubit systems
Energy Technology Data Exchange (ETDEWEB)
Montes, Enrique; Calero, Jesus M; Reina, John H, E-mail: enriquem@univalle.edu.c, E-mail: j.reina-estupinan@physics.ox.ac.u [Departamento de Fisica, Universidad del Valle, A.A. 25360, Cali (Colombia)
2009-05-01
We perform a theoretical study of composed superconducting qubit systems for the case of a coupled qubit configuration based on a hybrid qubit circuit made of both charge and phase qubits, which are coupled via a sigma{sub x} x sigma{sub z} interaction. We compute the system's eigen-energies in terms of the qubit transition frequencies and the strength of the inter-qubit coupling, and describe the sensitivity of the energy crossing/anti-crossing features to such coupling. We compute the hybrid system's dissipative dynamics for the cases of i) collective and ii) independent decoherence, whereby the system interacts with one common and two different baths of harmonic oscillators, respectively. The calculations have been performed within the Bloch-Redfield formalism and we report the solutions for the populations and the coherences of the system's reduced density matrix. The dephasing and relaxation rates are explicitly calculated as a function of the heat bath temperature.
Modelling supervisory controller for hybrid power systems
Energy Technology Data Exchange (ETDEWEB)
Pereira, A.; Bindner, H.; Lundsager, P. [Risoe National Lab., Roskilde (Denmark); Jannerup, O. [Technical Univ. of Denmark, Dept. of Automation, Lyngby (Denmark)
1999-03-01
Supervisory controllers are important to achieve optimal operation of hybrid power systems. The performance and economics of such systems depend mainly on the control strategy for switching on/off components. The modular concept described in this paper is an attempt to design standard supervisory controllers that could be used in different applications, such as village power and telecommunication applications. This paper presents some basic aspects of modelling and design of modular supervisory controllers using the object-oriented modelling technique. The functional abstraction hierarchy technique is used to formulate the control requirements and identify the functions of the control system. The modular algorithm is generic and flexible enough to be used with any system configuration and several goals (different applications). The modularity includes accepting modification of system configuration and goals during operation with minor or no changes in the supervisory controller. (au)
Hybrid holographic non-destructive test system
Kurtz, R. L. (Inventor)
1978-01-01
An automatic hybrid holographic non-destructive testing (HNDT) method and system capable of detecting flaws or debonds contained within certain materials are described. This system incorporates the techniques of optical holography, acoustical/optical holography and holographic correlation in determining the structural integrity of a test object. An automatic processing system including a detector and automatic data processor is used in conjunction with the three holographic techniques for correlating and interpreting the information supplied by the non-destructive systems. The automatic system also includes a sensor which directly translates an optical data format produced by the holographic techniques into electrical signals and then transmits this information to a digital computer for indicating the structural properties of the test object. The computer interprets the data gathered and determines whether further testing is necessary as well as the format of this new testing procedure.
Wang, Baijie; Wang, Xin; Chen, Zhangxin
2013-08-01
Reservoir characterization refers to the process of quantitatively assigning reservoir properties using all available field data. Artificial neural networks (ANN) have recently been introduced to solve reservoir characterization problems dealing with the complex underlying relationships inherent in well log data. Despite the utility of ANNs, the current limitation is that most existing applications simply focus on directly implementing existing ANN models instead of improving/customizing them to fit the specific reservoir characterization tasks at hand. In this paper, we propose a novel intelligent framework that integrates fuzzy ranking (FR) and multilayer perceptron (MLP) neural networks for reservoir characterization. FR can automatically identify a minimum subset of well log data as neural inputs, and the MLP is trained to learn the complex correlations from the selected well log data to a target reservoir property. FR guarantees the selection of the optimal subset of representative data from the overall well log data set for the characterization of a specific reservoir property; and, this implicitly improves the modeling and predication accuracy of the MLP. In addition, a growing number of industrial agencies are implementing geographic information systems (GIS) in field data management; and, we have designed the GFAR solution (GIS-based FR ANN Reservoir characterization solution) system, which integrates the proposed framework into a GIS system that provides an efficient characterization solution. Three separate petroleum wells from southwestern Alberta, Canada, were used in the presented case study of reservoir porosity characterization. Our experiments demonstrate that our method can generate reliable results.
Maze learning by a hybrid brain-computer system
Wu, Zhaohui; Zheng, Nenggan; Zhang, Shaowu; Zheng, Xiaoxiang; Gao, Liqiang; Su, Lijuan
2016-09-01
The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.
Biologically inspired neural network controller for an infrared tracking system
Frigo, Janette R.; Tilden, Mark W.
1999-01-01
Many biological system exhibit capable, adaptive behavior with a minimal nervous system such as those found in lower invertebrates. Scientists and engineers are studying biological system because these models may have real-world applications. the analog neural controller, herein, is loosely modeled after minimal biological nervous systems. The system consists of the controller and pair of sensor mounted on an actuator. It is implemented with an electrical oscillator network, two IR sensor and a dc motor, used as an actuator for the system. The system tracks an IR target source. The pointing accuracy of this neural network controller is estimated through experimental measurements and a numerical model of the system.
Bimode uninterruptible power supply compatibility in renewable hybrid energy systems
Energy Technology Data Exchange (ETDEWEB)
Bower, W. (Sandia National Labs., Albuquerque, NM (USA)); O' Sullivan, G. (Abacus Controls, Inc., Somerville, NJ (USA))
1990-08-01
Inverters installed in renewable hybrid energy systems are typically used in a stand-alone mode to supply ac power to loads from battery storage when the engine-generator is not being used. Similarities in topology and in the performance requirements of the standby uninterruptible power supply (UPS) system and the hybrid system suggest the UPS could be used in hybrid energy systems. Another alternative to inverters with add-on charging circuits or standby UPS hardware is the Bimode UPS. The bimode UPS uses common circuitry and power components for dc to ac inversion and battery charging. It also provides an automatic and nearly instantaneous ac power transfer function when the engine-generator is started or stopped. The measured operating and transfer characteristics of a bimode UPS in a utility system and in a hybrid system are presented. The applicability of the bimode UPS to hybrid systems and its compatibility in a PV/engine-generator hybrid system are given.
Optimization of Renewable Energy Hybrid System for Grid Connected Application
Directory of Open Access Journals (Sweden)
Mustaqimah Mustaqimah
2012-10-01
Full Text Available ABSTRACT. Hybrid energy systems are pollution free, takes low cost and less gestation period, user and social friendly. Such systems are important sources of energy for shops, schools, and clinics in village communities especially in remote areas. Hybrid systems can provide electricity at a comparatively economic price in many remote areas. This paper presents a method to jointly determine the sizing and operation control of hybrid energy systems. The model, PV wind hydro and biomass hybrid system connects to grid. The system configuration of the hybrid is derived based on a theoretical domestic load at a typical location and local solar radiation, wind and water flow rate data and biomass availability. The hybrid energy system is proposed for 10 of teacher’s houses of Industrial Training Institute, Mersing. It is predicted 10 kW load consumption per house. The hybrid energy system consists of wind, solar, biomass, hydro, and grid power. Approximately energy consumption is 860 kWh/day with a 105 kW peak demand load. The proposed hybrid renewable consists of solar photovoltaic (PV panels, wind turbine, hydro turbine and biomass. Battery and inverter are included as part of back-up and storage system. It provides the economic sensitivity of hybridization and the economic and environmental benefits of using a blend of technologies. It also presents the trade off that is involved in optimizing a hybrid energy system to harness and utilize the available renewable energy resources efficiently.
Hybrid quantum systems of atoms and ions
Zipkes, Christoph; Palzer, Stefan; Sias, Carlo; Köhl, Michael
2010-01-01
In recent years, ultracold atoms have emerged as an exceptionally controllable experimental system to investigate fundamental physics, ranging from quantum information science to simulations of condensed matter models. Here we go one step further and explore how cold atoms can be combined with other quantum systems to create new quantum hybrids with tailored properties. Coupling atomic quantum many-body states to an independently controllable single-particle gives access to a wealth of novel physics and to completely new detection and manipulation techniques. We report on recent experiments in which we have for the first time deterministically placed a single ion into an atomic Bose Einstein condensate. A trapped ion, which currently constitutes the most pristine single particle quantum system, can be observed and manipulated at the single particle level. In this single-particle/many-body composite quantum system we show sympathetic cooling of the ion and observe chemical reactions of single particles in situ...
Hybrid quantum systems of atoms and ions
Energy Technology Data Exchange (ETDEWEB)
Zipkes, Christoph; Ratschbacher, Lothar; Palzer, Stefan; Sias, Carlo; Koehl, Michael [Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE (United Kingdom)
2011-01-10
In recent years, ultracold atoms have emerged as an exceptionally controllable experimental system to investigate fundamental physics, ranging from quantum information science to simulations of condensed matter models. Here we go one step further and explore how cold atoms can be combined with other quantum systems to create new quantum hybrids with tailored properties. Coupling atomic quantum many-body states to an independently controllable single-particle gives access to a wealth of novel physics and to completely new detection and manipulation techniques. We report on recent experiments in which we have for the first time deterministically placed a single ion into an atomic Bose Einstein condensate. A trapped ion, which currently constitutes the most pristine single particle quantum system, can be observed and manipulated at the single particle level. In this single-particle/many-body composite quantum system we show sympathetic cooling of the ion and observe chemical reactions of single particles in situ.
Hybrid high power femtosecond laser system
Trunov, V. I.; Petrov, V. V.; Pestryakov, E. V.; Kirpichnikov, A. V.
2006-01-01
Design of a high-power femtosecond laser system based on hybrid chirped pulse amplification (CPA) technique developed by us is presented. The goal of the hybrid principle is the use of the parametric and laser amplification methods in chirped pulse amplifiers. It makes it possible to amplify the low-cycle pulses with a duration of <= fs to terawatt power with a high contrast and high conversion efficiency of the pump radiation. In a created system the Ti:Sapphire laser with 10 fs pulses at 810 nm and output energy about 1-3 nJ will be used like seed source. The oscillator pulses were stretched to duration of about 500 ps by an all-reflective grating stretcher. Then the stretched pulses are injected into a nondegenerate noncollinear optical parametric amplifier (NOPA) on the two BBO crystals. After amplification in NOPA the residual pump was used in a bow-tie four pass amplifier with hybrid active medium (based on Al II0 3:Ti 3+ and BeAl IIO 4:Ti 3+ crystals). The final stage of the amplification system consists of two channels, namely NIR (820 nm) and short-VIS (410 nm). Numerical simulation has shown that the terawatt level of output power can be achieved also in a short-VIS channel at the pumping of the double-crystal BBO NOPA by the radiation of the fourth harmonic of the Nd:YAG laser at 266 nm. Experimentally parametric amplification in BBO crystals of 30-50 fs pulses were investigated and optimized using SPIDER technique and single-shot autocomelator for the realization of shortest duration 40 fs.
Spontaneously Igniting Hybrid Fuel-Oxidiser Systems
Directory of Open Access Journals (Sweden)
S. R. Jain
1995-01-01
Full Text Available After briefly outlining the recent developments in hybrid rockets, the work carried out by the author on self-igniting (hypergolic solid fuel-liquid oxidiser systems has been reviewed. A major aspect relates to the solid derivatives of hydrazines, which have been conceived as fuels for hybrid rockets. Many of these N-N bonded compounds ignite readily, with very short ignition delays, on coming into contact with liquid oxidisers, like HNO/sub 3/ and N/sub 2/ O/sub 4/. The ignition characteristics have been examined as a function of the nature of the functional group in the fuel molecule, in an attempt to establish a basis for the hypergolic ignition in terms of chemical reactivity of the fuel-oxidiser combination. Important chemical reactions occurring in the pre-ignition stage have been identified by examining the quenched reaction products. Hybrid systems exhibiting synergistic hypergolicity in the presence of metal powders have investigated. An estimation of the rocket performance parameters, experimental determination of the heats of combustion in HNO/sub 3/, thermal decomposition characteristics, temperature profile by thin film thermometry and product identification by the rapid scan FT-IR, are among the other relevant studies made on these systems. A significant recent development has been the synthesis of new N-N bonded viscous binders, capable of rataining the hypergolicity of the fuel powders embedded therein as well as providing the required mechanical strength to the grain. Several of these resins have been characterised. Metallised fuel composites of these resins having high loading of magnesium are found to have short ignition delays and high performance parameters.
Interval standard neural network models for nonlinear systems
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A neural-network-based robust control design is suggested for control of a class of nonlinear systems. The design approach employs a neural network, whose activation functions satisfy the sector conditions, to approximate the nonlinear system. To improve the approximation performance and to account for the parameter perturbations during operation, a novel neural network model termed standard neural network model (SNNM) is proposed. If the uncertainty is bounded, the SNNM is called an interval SNNM (ISNNM). A state-feedback control law is designed for the nonlinear system modelled by an ISNNM such that the closed-loop system is globally, robustly, and asymptotically stable. The control design equations are shown to be a set of linear matrix inequalities (LMIs) that can be easily solved by available convex optimization algorithms. An example is given to illustrate the control design procedure, and the performance of the proposed approach is compared with that of a related method reported in literature.
Dopamine system: Manager of neural pathways
Directory of Open Access Journals (Sweden)
Simon eHong
2013-12-01
Full Text Available There are a growing number of roles that midbrain dopamine (DA neurons assume, such as, reward, aversion, alerting and vigor. Here I propose a theory that may be able to explain why the suggested functions of DA came about. It has been suggested that largely parallel cortico-basal ganglia-thalamo-cortico loops exist to control different aspects of behavior. I propose that (1 the midbrain DA system is organized in a similar manner, with different groups of DA neurons corresponding to these parallel neural pathways (NPs. The DA system can be viewed as the manager of these parallel NPs in that it recruits and activates only the task-relevant NPs when they are needed. It is likely that the functions of those NPs that have been consistently activated by the corresponding DA groups are facilitated. I also propose that (2 there are two levels of DA roles: the How and What roles. The How role is encoded in tonic and phasic DA neuron firing patterns and gives a directive to its target NP: how vigorously its function needs to be carried out. The tonic DA firing is to maintain a certain level of DA in the target NPs to support their expected behavioral and mental functions; it is only when a sudden unexpected boost or suppression of activity is required by the relevant target NP that DA neurons in the corresponding NP act in a phasic manner. The What role is the implementational aspect of the role of DA in the target NP, such as binding to D1 receptors to boost working memory. This What aspect of DA explains why DA seems to assume different functions depending on the region of the brain in which it is involved. In terms of the role of the lateral habenula (LHb, the LHb is expected to suppress maladaptive behaviors and mental processes by controlling the DA system. The demand-based smart management by the DA system may have given animals an edge in evolution with adaptive behaviors and a better survival rate in resource-scarce situations.
Superconducting qubit-resonator-atom hybrid system
Yu, Deshui; Kwek, Leong Chuan; Amico, Luigi; Dumke, Rainer
2017-09-01
We propose a hybrid quantum system where an LC resonator inductively interacts with a flux qubit and is capacitively coupled to a Rydberg atom. Varying the external magnetic flux bias controls the flux qubit flipping and the flux qubit-resonator interface. The atomic spectrum is tuned via an electrostatic field, manipulating the qubit-state transition of atom and the atom-resonator coupling. Different types of entanglement of superconducting, photonic and atomic qubits can be prepared via simply tuning the flux bias and electrostatic field, leading to the implementation of three-qubit Toffoli logic gate.
Directory of Open Access Journals (Sweden)
Tao Ma
2016-10-01
Full Text Available The development of intrusion detection systems (IDS that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC and deep neural network (DNN algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN, support vector machine (SVM, random forest (RF and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.
Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun
2016-10-13
The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.
Directory of Open Access Journals (Sweden)
J. C. Ochoa-Rivera
2002-01-01
Full Text Available A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain, while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2 model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation. Keywords: neural networks, perceptron multilayer, error backpropagation, hydrological scenario generation, multivariate time-series..
THE USE OF GENETIC ALGORITHM IN DIMENSIONING HYBRID AUTONOMOUS SYSTEMS
Directory of Open Access Journals (Sweden)
RUS T.
2016-03-01
Full Text Available In this paper is presented the working principle of genetic algorithms used to dimension autonomous hybrid systems. It is presented a study case in which is dimensioned and optimized an autonomous hybrid system for a residential house located in Cluj-Napoca. After the autonomous hybrid system optimization is performed, it is achieved a reduction of the total cost of system investment, a reduction of energy produced in excess and a reduction of CO2 emissions.
Wang, K W; Deng, C; Li, J P; Zhang, Y Y; Li, X Y; Wu, M C
2017-04-01
Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.
Vein matching using artificial neural network in vein authentication systems
Noori Hoshyar, Azadeh; Sulaiman, Riza
2011-10-01
Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.
Spiking Neural P Systems with Neuron Division and Budding
Pan, Linqiang; Paun, Gheorghe; Pérez Jiménez, Mario de Jesús
2009-01-01
In order to enhance the e±ciency of spiking neural P systems, we introduce the features of neuron division and neuron budding, which are processes inspired by neural stem cell division. As expected (as it is the case for P systems with active membranes), in this way we get the possibility to solve computationally hard problems in polynomial time. We illustrate this possibility with SAT problem.
Directory of Open Access Journals (Sweden)
Junwei Sun
2014-01-01
Full Text Available Some important dynamical properties of the memristor chaotic oscillator system have been studied in the paper. A novel hybrid dislocated control method and a general hybrid projective dislocated synchronization scheme have been realized for memristor chaotic oscillator system. The paper firstly presents hybrid dislocated control method for stabilizing chaos to the unstable equilibrium point. Based on the Lyapunov stability theorem, general hybrid projective dislocated synchronization has been studied for the drive memristor chaotic oscillator system and the same response memristor chaotic oscillator system. For the different dimensions, the memristor chaotic oscillator system and the other chaotic system have realized general hybrid projective dislocated synchronization. Numerical simulations are given to show the effectiveness of these methods.
Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.
2000-01-01
Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.
A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels
Directory of Open Access Journals (Sweden)
Uttam Kumar
2012-09-01
Full Text Available Signals acquired by sensors in the real world are non-linear combinations, requiring non-linear mixture models to describe the resultant mixture spectra for the endmember’s (pure pixel’s distribution. This communication discusses inferring class fraction through a novel hybrid mixture model (HMM. HMM is a three-step process, where the endmembers are first derived from the images themselves using the N-FINDR algorithm. These endmembers are used by the linear mixture model (LMM in the second step that provides an abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual ground proportions are fed into neural network based multi-layer perceptron (MLP architecture as input to train the neurons. The neural output further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. HMM is first implemented and validated on simulated hyper spectral data of 200 bands and subsequently on real time MODIS data with a spatial resolution of 250 m. The results on computer simulated data show that the method gives acceptable results for unmixing pixels with an overall RMSE of 0.0089 ± 0.0022 with LMM and 0.0030 ± 0.0001 with the HMM when compared to actual class proportions. The unmixed MODIS images showed overall RMSE with HMM as 0.0191 ± 0.022 as compared to the LMM output considered alone that had an overall RMSE of 0.2005 ± 0.41, indicating that individual class abundances obtained from HMM are very close to the real observations.
Nuclear Hybrid Energy Systems: Challenges and Opportunities
Energy Technology Data Exchange (ETDEWEB)
P. Sabharwall; S.B. Sitton; S.J. Yoon; C. Stoots
2014-07-01
With growing demand of energy and costs of the fossil fuels, coupled with the environmental concerns have resulted in an increased interest in alternative energy sources. Nuclear hybrid energy systems (NHES) are being considered which incorporates renewable energy sources such as solar and wind energy combined with nuclear reactor and energy storage to meet the peak hours demand imposed on the grid, along with providing process heat for other potential industrial applications. This concept could potentially satisfy various energy demands and improve reliability, robustness and resilience for the entire system as a whole, along with economic and net efficiency gains. This paper provides a brief understanding of potential NHES system and architecture along with the challenges
Directory of Open Access Journals (Sweden)
Babankumar S. Bansod
2011-02-01
Full Text Available Starting from descriptive data on crop yield and various other properties, the aim of this study is to reveal the trends on soil behaviour, such as crop yield. This study has been carried out by developing web application that uses a well known technique- Cluster Analysis. The cluster analysis revealed linkages between soil classes for the same field as well as between different fields, which can be partly assigned to crops rotation and determination of variable soil input rates. A hybrid clustering algorithm has been developed taking into account the traits of two clustering technologies: i Hierarchical clustering, ii K-means clustering. This hybrid clustering algorithm is applied to sensor- gathered data about soil and analysed, resulting in the formation of well delineatedmanagement zones based on various properties of soil, such as, ECa , crop yield, etc. One of the purposes of the study was to identify the main factors affecting the crop yield and the results obtained were validated with existing techniques. To accomplish this purpose, geo-referenced soil information has been examined. Also, based on this data, statistical method has been used to classify and characterize the soil behaviour. This is done using a prediction model, developed to predict the unknown behaviour of clusters based on the known behaviour of other clusters. In predictive modeling, data has been collected for the relevant predictors, a statistical model has been formulated, predictions were made and the model can be validated (or revised as additional data becomes available. The model used in the web application has been formed taking into account neural network based minimum hamming distance criterion.
Intelligent Control Scheme of Engineering Machinery of Cluster Hybrid System
Institute of Scientific and Technical Information of China (English)
GAO Qiang; WANG Hongli
2005-01-01
In a hybrid system, the subsystems with discrete dynamics play a central role in a hybrid system. In the course of engineering machinery of cluster construction, the discrete control law is hard to obtain because the construction environment is complex and there exist many affecting factors. In this paper, hierarchically intelligent control, expert control and fuzzy control are introduced into the discrete subsystems of engineering machinery of cluster hybrid system, so as to rebuild the hybrid system and make the discrete control law easily and effectively obtained. The structures, reasoning mechanism and arithmetic of intelligent control are replanted to discrete dynamic, conti-nuous process and the interface of the hybrid system. The structures of three types of intelligent hybrid system are presented and the human experiences summarized from engineering machinery of cluster are taken into account.
Indoor Positioning System Using Artificial Neural Network
Directory of Open Access Journals (Sweden)
Hamid Mehmood
2010-01-01
Full Text Available Problem statement: Location knowledge in indoor environment using Indoor Positioning Systems (IPS has become very useful and popular in recent years. A number of Location Based Services (LBS have been developed, which are based on IPS, these LBS include asset tracking, inventory management and security based applications. Many next-generation LBS applications such as social networking, local search, advertising and geo-tagging are expected to be used in urban and indoor environments where GNSS either underperforms in terms of fix times or accuracy, or fails altogether. To develop an IPS based on Wi-Fi Received Signal Strength (RSS using Artificial Neural Networks (ANN, which should use already available Wi-Fi infrastructure in a heterogeneous environment. Approach: This study discussed the use of ANN for IPS using RSS in an indoor wireless facility which has varying human activity, material of walls and type of Wireless Access Points (WAP, hence simulating a heterogeneous environment. The proposed system used backpropogation method with 4 input neurons, 2 output neurons and 4 hidden layers. The model was trained with three different types of training data. The accuracy assessment for each training data was performed by computing the distance error and average distance error. Results: The results of the experiments showed that using ANN with the proposed method of collecting training data, maximum accuracy of 0.7 m can be achieved, with 30% of the distance error less than 1 m and 60% of the distance error within the range of 1-2 m. Whereas maximum accuracy of 1.01 can be achieved with the commonly used method of collecting training data. The proposed model also showed 67% more accuracy as compared to a probabilistic model. Conclusion: The results indicated that ANN based IPS can provide accuracy and precision which is quite adequate for the development of indoor LBS while using the already available Wi-Fi infrastructure, also the proposed method
Hybrid intelligent PID control design for PEMFC anode system
Institute of Scientific and Technical Information of China (English)
Rui-min WANG; Ying-ying ZHANG; Guang-yi CAO
2008-01-01
Control design is important for proton exchange membrane fuel cell (PEMFC) generator. This work researched the anode system of a 60-kW PEMFC generator. Both anode pressure and humidity must he maintained at ideal levels during steady operation. In view of characteristics and requirements of the system, a hybrid intelligent PID controller is designed specifically based on dynamic simulation. A single neuron PI controller is used for anode humidity by adjusting the water injection to the hydrogen cell. Another incremental PID controller, based on the diagonal recurrent neural network (DRNN) dynamic identification, is used to control anode pressure to be more stable and exact by adjusting the hydrogen flow rate. This control strategy can avoid the coupling problem of the PEMFC and achieve a more adaptive ability. Simulation results showed that the control strategy can maintain both anode humidity and pressure at ideal levels regardless of variable load, nonlinear dynamic and coupling characteristics of the system. This work will give some guides for further control design and applications of the total PEMFC generator.
Automation Comparison Procedure for Verification of Hybrid Systems.
1997-11-01
Lecture Notes in Computer Science vol. 999, Springer-Verlag, (1995). [2] Bowler, 0., Grotzky, J., Nielson, M., Nilson.S., and Van Buren, J...Remmel, J.B., "Feedback Derivations: Near Optimal Controls for Hybrid Systems", to appear in Hybrid Systems III, Springer Lecture Notes in Computer Science . [6...Grossman, R.L., Nerode, A., Ravn, A. and Rischel, H. eds., Hybrid Systems, Lecture Notes in Computer
Implementations of learning control systems using neural networks
Sartori, Michael A.; Antsaklis, Panos J.
1992-01-01
The systematic storage in neural networks of prior information to be used in the design of various control subsystems is investigated. Assuming that the prior information is available in a certain form (namely, input/output data points and specifications between the data points), a particular neural network and a corresponding parameter design method are introduced. The proposed neural network addresses the issue of effectively using prior information in the areas of dynamical system (plant and controller) modeling, fault detection and identification, information extraction, and control law scheduling.
Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold
2017-08-11
This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.
Simulation of Noise-Cancelling in the Cockpit of an Aircraft Using Two-Rate Hybrid Neural Network
Directory of Open Access Journals (Sweden)
ASTROV, I.
2007-11-01
Full Text Available This paper presents the two-rate hybrid neural network (TRHNN for processing of noisy signal. The received TRHNN consists of "fast" ADAptive LInear NEuron neural network (FADALINENN and "slow" radial basis neural network (SRBNN. The illustrative design example - noise-cancelling of noisy pilot's voice pattern - was carried out using the TRHNN. The received TRHNN has high speed of signal processing. This example demonstrates that the proposed TRHNN is capable not only to recognize the pilot's voice in the noisy voice pattern, but also to restore the pilot's voice. The simulation results with use the software package Simulink show the computing procedure and applicability of the TRHNNs for fast-acting signal processing and analysis in real-time flight conditions.
DISPATCHING OF HYBRID WINDPHOTOVOLTAIC- MICROTURBINESSTORAGE SYSTEM
Directory of Open Access Journals (Sweden)
Dr. BRINI SAOUSSEN
2012-06-01
Full Text Available This paper presents model for Economic Environmental Dispatching (EED of Hybrid power system including wind and photovoltaic energies. The model combines the stochastic data of the climate such as the wind speed for wind energy, solar radiation and the temperature for photovoltaic energy. The penetration of wind andphotovoltaic powers into traditional network will cause some implications such as security concerns due to its unpredictable nature. The production systems of renewable energy are generally coupled with the network with energy storage devices and micro sources. In this paper, a bi-objective economic environmental dispatchproblem considering wind and photovoltaic penetration is formulated, which treats economic and environmental impacts as conflicting objectives. It applied multiobjective optimization by approach SPEA to solve (EED problem.
Infectious disease modeling a hybrid system approach
Liu, Xinzhi
2017-01-01
This volume presents infectious diseases modeled mathematically, taking seasonality and changes in population behavior into account, using a switched and hybrid systems framework. The scope of coverage includes background on mathematical epidemiology, including classical formulations and results; a motivation for seasonal effects and changes in population behavior, an investigation into term-time forced epidemic models with switching parameters, and a detailed account of several different control strategies. The main goal is to study these models theoretically and to establish conditions under which eradication or persistence of the disease is guaranteed. In doing so, the long-term behavior of the models is determined through mathematical techniques from switched systems theory. Numerical simulations are also given to augment and illustrate the theoretical results and to help study the efficacy of the control schemes.
Control of Unknown Chaotic Systems Based on Neural Predictive Control
Institute of Scientific and Technical Information of China (English)
LI Dong-Mei; WANG Zheng-Ou
2003-01-01
We introduce the predictive control into the control of chaotic system and propose a neural networkcontrol algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknownchaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is muchhigher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of thecontrol system and prove the convergence property of the neural controller. The theoretic derivation and simulationsdemonstrate the effectiveness of the algorithm.
Control of Unknown Chaotic Systems Based on Neural Predictive Control
Institute of Scientific and Technical Information of China (English)
LIDong-Mei; WANGZheng-Ou
2003-01-01
We introduce the predictive control into the control of chaotic system and propose a neural network control algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is much higher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of the control system and prove the convergence property of the neural controller. The theoretic derivation and simulations demonstrate the effectiveness of the algorithm.
A survey on RBF Neural Network for Intrusion Detection System
Directory of Open Access Journals (Sweden)
Henali Sheth
2014-12-01
Full Text Available Network security is a hot burning issue nowadays. With the help of technology advancement intruders or hackers are adopting new methods to create different attacks in order to harm network security. Intrusion detection system (IDS is a kind of security software which inspects all incoming and outgoing network traffic and it will generate alerts if any attack or unusual behavior is found in a network. Various approaches are used for IDS such as data mining, neural network, genetic and statistical approach. Among this Neural Network is more suitable approach for IDS. This paper describes RBF neural network approach for Intrusion detection system. RBF is a feed forward and supervise technique of neural network.RBF approach has good classification ability but its performance depends on its parameters. Based on survey we find that RBF approach has some short comings. In order to overcome this we need to do proper optimization of RBF parameters.
Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method
Furferi, Rocco; Governi, Lapo; Volpe, Yary
2016-11-01
Color matching of fabric blends is a key issue for the textile industry, mainly due to the rising need to create high-quality products for the fashion market. The process of mixing together differently colored fibers to match a desired color is usually performed by using some historical recipes, skillfully managed by company colorists. More often than desired, the first attempt in creating a blend is not satisfactory, thus requiring the experts to spend efforts in changing the recipe with a trial-and-error process. To confront this issue, a number of computer-based methods have been proposed in the last decades, roughly classified into theoretical and artificial neural network (ANN)-based approaches. Inspired by the above literature, the present paper provides a method for accurate estimation of spectrophotometric response of a textile blend composed of differently colored fibers made of different materials. In particular, the performance of the Kubelka-Munk (K-M) theory is enhanced by introducing an artificial intelligence approach to determine a more consistent value of the nonlinear function relationship between the blend and its components. Therefore, a hybrid K-M+ANN-based method capable of modeling the color mixing mechanism is devised to predict the reflectance values of a blend.
Directory of Open Access Journals (Sweden)
Montri Inthachot
2016-01-01
Full Text Available This study investigated the use of Artificial Neural Network (ANN and Genetic Algorithm (GA for prediction of Thailand’s SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid’s prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.
Production of Engineered Fabrics Using Artificial Neural Network-Genetic Algorithm Hybrid Model
Mitra, Ashis; Majumdar, Prabal Kumar; Banerjee, Debamalya
2015-10-01
The process of fabric engineering which is generally practised in most of the textile mills is very complicated, repetitive, tedious and time consuming. To eliminate this trial and error approach, a new approach of fabric engineering has been attempted in this work. Data sets of construction parameters [comprising of ends per inch, picks per inch, warp count and weft count] and three fabric properties (namely drape coefficient, air permeability and thermal resistance) of 25 handloom cotton fabrics have been used. The weights and biases of three artificial neural network (ANN) models developed for the prediction of drape coefficient, air permeability and thermal resistance were used to formulate the fitness or objective function and constraints of the optimization problem. The optimization problem was solved using genetic algorithm (GA). In both the fabrics which were attempted for engineering, the target and simulated fabric properties were very close. The GA was able to search the optimum set of fabric construction parameters with reasonably good accuracy except in case of EPI. However, the overall result is encouraging and can be improved further by using larger data sets of handloom fabrics by hybrid ANN-GA model.
Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm-Artificial Neural Network.
Ramadan Suleiman, Ahmed; Nehdi, Moncef L
2017-02-07
This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm-artificial neural network (GA-ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA-ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials.
Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network
Directory of Open Access Journals (Sweden)
Ahmed Ramadan Suleiman
2017-02-01
Full Text Available This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN. A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c, type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components on the self-healing performance in cement-based materials.
Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network
Ramadan Suleiman, Ahmed; Nehdi, Moncef L.
2017-01-01
This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials. PMID:28772495
The Criticality Hypothesis in Neural Systems
Karimipanah, Yahya
There is mounting evidence that neural networks of the cerebral cortex exhibit scale invariant dynamics. At the larger scale, fMRI recordings have shown evidence for spatiotemporal long range correlations. On the other hand, at the smaller scales this scale invariance is marked by the power law distribution of the size and duration of spontaneous bursts of activity, which are referred as neuronal avalanches. The existence of such avalanches has been confirmed by several studies in vitro and in vivo, among different species and across multiple scales, from spatial scale of MEG and EEG down to single cell resolution. This prevalent scale free nature of cortical activity suggests the hypothesis that the cortex resides at a critical state between two phases of order (short-lasting activity) and disorder (long-lasting activity). In addition, it has been shown, both theoretically and experimentally, that being at criticality brings about certain functional advantages for information processing. However, despite the plenty of evidence and plausibility of the neural criticality hypothesis, still very little is known on how the brain may leverage such criticality to facilitate neural coding. Moreover, the emergent functions that may arise from critical dynamics is poorly understood. In the first part of this thesis, we review several pieces of evidence for the neural criticality hypothesis at different scales, as well as some of the most popular theories of self-organized criticality (SOC). Thereafter, we will focus on the most prominent evidence from small scales, namely neuronal avalanches. We will explore the effect of adaptation and how it can maintain scale free dynamics even at the presence of external stimuli. Using calcium imaging we also experimentally demonstrate the existence of scale free activity at the cellular resolution in vivo. Moreover, by exploring the subsampling issue in neural data, we will find some fundamental constraints of the conventional methods
Development of Traction Drive Motors for the Toyota Hybrid System
Kamiya, Munehiro
Toyota Motor Corporation developed in 2005 a new hybrid system for a large SUV. This system included the new development of a high-speed traction drive motor achieving a significant increase in power weight ratio. This paper provides an overview of the hybrid system, discusses the characteristics required of a traction drive motor, and presents the technologies employed in the developed motor.
Data Process of Diagnose Expert System based on Neural Network
Directory of Open Access Journals (Sweden)
Shupeng Zhao
2013-12-01
Full Text Available Engine fault has a high rate in the car. Considering about the distinguishing feature of the engine, Engine Diagnosis Expert System was investigated based on Diagnosis Tree module, Fuzzy Neural Network module, and commix reasoning module. It was researched including Knowledge base and Reasoning machine, and so on. In Diagnosis Tree module, the origin problem was searched in right method. In which module distinguishing rate and low error and least cost was the aim. By means of synthesize judge and fuzzy relation reasoning to get fault origin from symptom, fuzzy synthesize reasoning diagnosis module was researched. Expert knowledge included failure symptom, engine system failure and engine part failure. In the system, Self-diagnosis method and general instruments method worked together, complex failure diagnosis became efficient. The system was intelligent, which was combined by fuzzy logic reasoning and the traditional neural network system. And it became more convenience for failure origin searching, because of utilizing the three methods. The system fuzzy neural networks were combined with fuzzy reasoning and traditional neural networks. Fuzzy neural network failure diagnosis module of system, as a important model was applied to engine diagnosis, with more advantages such as higher efficiency of searching and higher self-learning ability, which was compared with the traditional BP network
Wang, H. B.; Li, J. W.; Zhou, B.; Yuan, Z. Q.; Chen, Y. P.
2013-03-01
In the last few decades, the development of Geographical Information Systems (GIS) technology has provided a method for the evaluation of landslide susceptibility and hazard. Slope units were found to be appropriate for the fundamental morphological elements in landslide susceptibility evaluation. Following the DEM construction in a loess area susceptible to landslides, the direct-reverse DEM technology was employed to generate 216 slope units in the studied area. After a detailed investigation, the landslide inventory was mapped in which 39 landslides, including paleo-landslides, old landslides and recent landslides, were present. Of the 216 slope units, 123 involved landslides. To analyze the mechanism of these landslides, six environmental factors were selected to evaluate landslide occurrence: slope angle, aspect, the height and shape of the slope, distance to river and human activities. These factors were extracted in terms of the slope unit within the ArcGIS software. The spatial analysis demonstrates that most of the landslides are located on convex slopes at an elevation of 100-150 m with slope angles from 135°-225° and 40°-60°. Landslide occurrence was then checked according to these environmental factors using an artificial neural network with back propagation, optimized by genetic algorithms. A dataset of 120 slope units was chosen for training the neural network model, i.e., 80 units with landslide presence and 40 units without landslide presence. The parameters of genetic algorithms and neural networks were then set: population size of 100, crossover probability of 0.65, mutation probability of 0.01, momentum factor of 0.60, learning rate of 0.7, max learning number of 10 000, and target error of 0.000001. After training on the datasets, the susceptibility of landslides was mapped for the land-use plan and hazard mitigation. Comparing the susceptibility map with landslide inventory, it was noted that the prediction accuracy of landslide occurrence
Limit Cycle Analysis in a Class of Hybrid Systems
Directory of Open Access Journals (Sweden)
Antonio Favela-Contreras
2016-01-01
Full Text Available Hybrid systems are those that inherently combine discrete and continuous dynamics. This paper considers the hybrid system model to be an extension of the discrete automata associating a continuous evolution with each discrete state. This model is called the hybrid automaton. In this work, we achieve a mathematical formulation of the steady state and we show a way to obtain the initial conditions region to reach a specific limit cycle for a class of uncoupled and coupled continuous-linear hybrid systems. The continuous-linear term is used in the sense of the system theory and, in this sense, continuous-linear hybrid automata will be defined. Thus, some properties and theorems that govern the hybrid automata dynamic behavior to evaluate a limit cycle existence have been established; this content is explained under a theoretical framework.
Nonlinear system identification based on internal recurrent neural networks.
Puscasu, Gheorghe; Codres, Bogdan; Stancu, Alexandru; Murariu, Gabriel
2009-04-01
A novel approach for nonlinear complex system identification based on internal recurrent neural networks (IRNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This approach employs internal state estimation when no measurements coming from the sensors are available for the system states. A modified backpropagation algorithm is introduced in order to train the IRNN for nonlinear system identification. The performance of the proposed design approach is proven on a car simulator case study.
Representation of neural networks as Lotka-Volterra systems
Moreau, Yves; Louiès, Stéphane; Vandewalle, Joos; Brenig, Léon
1999-03-01
We study changes of coordinates that allow the representation of the ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models—also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form, where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoïd. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network.
Data Mining and Neural Network Techniques in Case Based System
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
This paper first puts forward a case-based system framework basedon data mining techniques. Then the paper examines the possibility of using neural n etworks as a method of retrieval in such a case-based system. In this system we propose data mining algorithms to discover case knowledge and other algorithms.
On a Variational Approach to Optimization of Hybrid Mechanical Systems
Directory of Open Access Journals (Sweden)
Vadim Azhmyakov
2010-01-01
Full Text Available This paper deals with multiobjective optimization techniques for a class of hybrid optimal control problems in mechanical systems. We deal with general nonlinear hybrid control systems described by boundary-value problems associated with hybrid-type Euler-Lagrange or Hamilton equations. The variational structure of the corresponding solutions makes it possible to reduce the original “mechanical” problem to an auxiliary multiobjective programming reformulation. This approach motivates possible applications of theoretical and computational results from multiobjective optimization related to the original dynamical optimization problem. We consider first order optimality conditions for optimal control problems governed by hybrid mechanical systems and also discuss some conceptual algorithms.
The Gwaii Haanas PV hybrid system : analysis of system operation
Energy Technology Data Exchange (ETDEWEB)
Ross, M. [GPCo Inc., Varennes, PQ (Canada); Turcotte, D.; Sheriff, F. [Natural Resources Canada, Varennes, PQ (Canada). CANMET Energy Technology Centre
2003-05-01
The operation of the photovoltaic/battery/genset hybrid power system at the Gwaii Haanas National Park Reserve warden station in British Columbia has been monitored since July 2001 as part of the Canada Centre for Mineral and Energy Technology (CANMET) Energy Technology Centre-Varennes Photovoltaic Hybrid Power Systems Program. The data collected has been used to validate hybrid system simulation tools being developed under the sponsorship of the Program. The analyzed data, along with the simulation tools, provided insight into the operation of the Gwaii Haanas power system. It also assisted in identifying the strengths and weaknesses of the system. Data indicates that the system functions well and is appropriately dimensioned and configured. The modules are arranged in two sub-arrays, with modules connected in parallel, showing remarkable tolerance to shading of part of the array. Almost complete discharge of the batteries occurs during the winter, when the Park residence is unoccupied. During the summer, users should keep track of the battery state-of-charge. Some recommendations were made to prolong the battery life. 1 ref., 1 tab., 16 figs.
Artificial immune system based neural networks for solving multi-objective programming problems
Directory of Open Access Journals (Sweden)
Waiel F. Abd El-Wahed
2010-12-01
Full Text Available In this paper, a hybrid artificial intelligent approach based on the clonal selection principle of artificial immune system (AIS and neural networks is proposed to solve multi-objective programming problems. Due to the sensitivity to the initial values of initial population of antibodies (Ab’s, neural networks is used to initialize the boundary of the antibodies for AIS to guarantee that all the initial population of Ab’s is feasible. The proposed approach uses dominance principle and feasibility to identify solutions that deserve to be cloned, and uses two types of mutation: uniform mutation is applied to the clones produced and non-uniform mutation is applied to the “not so good” antibodies. A secondary (or external population that stores the nondominated solutions found along the search process is used. Such secondary population constitutes the elitist mechanism of our approach and it allows it to move towards the Pareto front.
Thermal photovoltaic solar integrated system analysis using neural networks
Energy Technology Data Exchange (ETDEWEB)
Ashhab, S. [Hashemite Univ., Zarqa (Jordan). Dept. of Mechanical Engineering
2007-07-01
The energy demand in Jordan is primarily met by petroleum products. As such, the development of renewable energy systems is quite attractive. In particular, solar energy is a promising renewable energy source in Jordan and has been used for food canning, paper production, air-conditioning and sterilization. Artificial neural networks (ANNs) have received significant attention due to their capabilities in forecasting, modelling of complex nonlinear systems and control. ANNs have been used for forecasting solar energy. This paper presented a study that examined a thermal photovoltaic solar integrated system that was built in Jordan. Historical input-output system data that was collected experimentally was used to train an ANN that predicted the collector, PV module, pump and total efficiencies. The model predicted the efficiencies well and can therefore be utilized to find the operating conditions of the system that will produce the maximum system efficiencies. The paper provided a description of the photovoltaic solar system including equations for PV module efficiency; pump efficiency; and total efficiency. The paper also presented data relevant to the system performance and neural networks. The results of a neural net model were also presented based on the thermal PV solar integrated system data that was collected. It was concluded that the neural net model of the thermal photovoltaic solar integrated system set the background for achieving the best system performance. 10 refs., 6 figs.
System Identification, Prediction, Simulation and Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1997-01-01
The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...... a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System...
OCT detection of neural activity in American cockroach nervous system
Gorczyńska, Iwona; Wyszkowska, Joanna; Bukowska, Danuta; Ruminski, Daniel; Karnowski, Karol; Stankiewicz, Maria; Wojtkowski, Maciej
2013-03-01
We show results of a project which focuses on detection of activity in neural tissue with Optical Coherence Tomography (OCT) methods. Experiments were performed in neural cords dissected from the American cockroach (Periplaneta americana L.). Functional OCT imaging was performed with ultrahigh resolution spectral / Fourier domain OCT system (axial resolution 2.5 μm). Electrical stimulation (voltage pulses) was applied to the sensory cercal nerve of the neural cord. Optical detection of functional activation of the sample was performed in the connective between the terminal abdominal ganglion and the fifth abdominal ganglion. Functional OCT data were collected over time with the OCT beam illuminating selected single point in the connectives (i.e. OCT M-scans were acquired). Phase changes of the OCT signal were analyzed to visualize occurrence of activation in the neural cord. Electrophysiology recordings (microelectrode method) were also performed as a reference method to demonstrate electrical response of the sample to stimulation.
System partitioning on MCM using a new neural network model
Institute of Scientific and Technical Information of China (English)
胡卫明; 徐俊华; 严晓浪; 何志钧
1999-01-01
A new self-organizing neural network model is presented, which can get rid of some fatal defects facing the Kohonen self-organizing neural network, known as the slow training speed, difficulty in designing neighboring zone, and disability to deal with area constraints directly. Based on the new neural network, a new approach for performance-driven system partitioning on MCM is presented. In the algorithm, the total routing cost between the chips and the circle time are both minimized, while satisfying area and timing constraints. The neural network has a reasonable structure and its training speed is high. The algorithm is able to deal with the large scale circuit partitioning, and has total optimization effect. The algorithm is programmed with Visual C + + language, and experimental result shows that it is an effective method.
Site characterization for hybrid system construction
Energy Technology Data Exchange (ETDEWEB)
Saldana, R.; Miranda, U.; Medrano, M. C. [Instituto de Investigaciones Electricas, Cuernavaca (Mexico)
1997-12-31
The basic reason to use alternative systems for electricity generation, in most cases, is the lack of electricity services, such as isolated rural communities which are located far away from the electric distribution line, and the cost of its extension is too expensive, while decentralized power systems can be an economic and appropriate solution to providing these services. Up to now there are several technological options for rural electrification using PV modules, wind plants, water-power plants, anaerobic digesters, or a combination of some of them, according to the availability of energetic resources. The applications include centralized or decentralized systems, autonomous or hybrid systems, isolated or interconnected to the electric line, etc. A particular hybrid system design can be done considering two general aspects, first it is necessary to know the electric consumption that will be supplied, taking into account present and future necessities and how local energetic resources are present in a selected site. Finally, also it is necessary to carry out an economic analysis to determine the cost of kilowatt-hour generated using local energetic resources and compare it with the cost of electricity produced by conventional power systems. [Espanol] La razon principal para el uso de sistemas alternativos de generacion de electricidad, en la mayoria de los casos, es la falta de servicios de electricidad, tal como en las comunidades rurales aisladas localizadas lejos de linea de distribucion electrica, donde el costo de su extension es demasiado caro, mientras que los sistemas descentralizados de energia pueden ser una solucion economica y adecuada para proporcionar estos servicios. Hasta ahora existen varias opciones tecnologicas para la electrificacion rural usando modulos fotovoltaicos, aerogeneradores, plantas hidroelectricas, digestores anaerobicos o una combinacion de algunos de ellos, de acuerdo con la disponibilidad de los recursos energeticos. Las
DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
Timur Kartbayev
2017-03-01
Full Text Available The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging. The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.
Directory of Open Access Journals (Sweden)
You Zhu
2016-05-01
Full Text Available Based on logistic regression (LR and artificial neural network (ANN methods, we construct an LR model, an ANN model and three types of a two-stage hybrid model. The two-stage hybrid model is integrated by the LR and ANN approaches. We predict the credit risk of China’s small and medium-sized enterprises (SMEs for financial institutions (FIs in the supply chain financing (SCF by applying the above models. In the empirical analysis, the quarterly financial and non-financial data of 77 listed SMEs and 11 listed core enterprises (CEs in the period of 2012–2013 are chosen as the samples. The empirical results show that: (i the “negative signal” prediction accuracy ratio of the ANN model is better than that of LR model; (ii the two-stage hybrid model type I has a better performance of predicting “positive signals” than that of the ANN model; (iii the two-stage hybrid model type II has a stronger ability both in aspects of predicting “positive signals” and “negative signals” than that of the two-stage hybrid model type I; and (iv “negative signal” predictive power of the two-stage hybrid model type III is stronger than that of the two-stage hybrid model type II. In summary, the two-stage hybrid model III has the best classification capability to forecast SMEs credit risk in SCF, which can be a useful prediction tool for China’s FIs.
FPGA implementation of a pyramidal Weightless Neural Networks learning system.
Al-Alawi, Raida
2003-08-01
A hardware architecture of a Probabilistic Logic Neuron (PLN) is presented. The suggested model facilitates the on-chip learning of pyramidal Weightless Neural Networks using a modified probabilistic search reward/penalty training algorithm. The penalization strategy of the training algorithm depends on a predefined parameter called the probabilistic search interval. A complete Weightless Neural Network (WNN) learning system is modeled and implemented on Xilinx XC4005E Field Programmable Gate Array (FPGA), allowing its architecture to be configurable. Various experiments have been conducted to examine the feasibility and performance of the WNN learning system. Results show that the system has a fast convergence rate and good generalization ability.
Parallel Hybrid Vehicle Optimal Storage System
Bloomfield, Aaron P.
2009-01-01
A paper reports the results of a Hybrid Diesel Vehicle Project focused on a parallel hybrid configuration suitable for diesel-powered, medium-sized, commercial vehicles commonly used for parcel delivery and shuttle buses, as the missions of these types of vehicles require frequent stops. During these stops, electric hybridization can effectively recover the vehicle's kinetic energy during the deceleration, store it onboard, and then use that energy to assist in the subsequent acceleration.
A Future with Hybrid Electric Propulsion Systems: A NASA Perspective
DelRosario, Ruben
2014-01-01
The presentation highlights a NASA perspective on Hybrid Electric Propulsion Systems for aeronautical applications. Discussed are results from NASA Advance Concepts Study for Aircraft Entering service in 2030 and beyond and the potential use of hybrid electric propulsion systems as a potential solution to the requirements for energy efficiency and environmental compatibility. Current progress and notional potential NASA research plans are presented.
HOPIS: hybrid omnidirectional and perspective imaging system for mobile robots.
Lin, Huei-Yung; Wang, Min-Liang
2014-09-04
In this paper, we present a framework for the hybrid omnidirectional and perspective robot vision system. Based on the hybrid imaging geometry, a generalized stereo approach is developed via the construction of virtual cameras. It is then used to rectify the hybrid image pair using the perspective projection model. The proposed method not only simplifies the computation of epipolar geometry for the hybrid imaging system, but also facilitates the stereo matching between the heterogeneous image formation. Experimental results for both the synthetic data and real scene images have demonstrated the feasibility of our approach.
Sun, Jitao; Lin, Hai
2008-09-01
This paper investigates the stationary oscillation for an impulsive delayed system which represents a class of nonlinear hybrid systems. First, a new concept of S-stability is introduced for nonlinear impulsive delayed systems. Based on this new concept and fixed point theorem, the relationship between S-stability and stationary oscillation (i.e., existence, uniqueness and global stability of periodic solutions) for the nonlinear impulsive delayed system is explored. It is shown that the nonlinear impulsive delayed system has a stationary oscillation if the system is S-stable. Second, an easily verifiable sufficient condition is then obtained for stationary oscillations of nonautonomous neural networks with both time delays and impulses by using the new criterion. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.
NNSYSID - toolbox for system identification with neural networks
DEFF Research Database (Denmark)
Norgaard, M.; Ravn, Ole; Poulsen, Niels Kjølstad
2002-01-01
The NNSYSID toolset for System Identification has been developed as an add on to MATLAB(R). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms...
NNSYSID - toolbox for system identification with neural networks
DEFF Research Database (Denmark)
Norgaard, M.; Ravn, Ole; Poulsen, Niels Kjølstad
2002-01-01
The NNSYSID toolset for System Identification has been developed as an add on to MATLAB(R). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms...
Wu, Guang; Dong, Zuomin
2017-09-01
Hybrid electric vehicles are widely accepted as a promising short to mid-term technical solution due to noticeably improved efficiency and lower emissions at competitive costs. In recent years, various hybrid powertrain systems were proposed and implemented based on different types of conventional transmission. Power-split system, including Toyota Hybrid System and Ford Hybrid System, are well-known examples. However, their relatively low torque capacity, and the drive of alternative and more advanced designs encouraged other innovative hybrid system designs. In this work, a new type of hybrid powertrain system based hybridized automated manual transmission (HAMT) is proposed. By using the concept of torque gap filler (TGF), this new hybrid powertrain type has the potential to overcome issue of torque gap during gearshift. The HAMT design (patent pending) is described in details, from gear layout and design of gear ratios (EV mode and HEV mode) to torque paths at different gears. As an analytical tool, mutli-body model of vehicle equipped with this HAMT was built to analyze powertrain dynamics at various steady and transient modes. A gearshift was decomposed and analyzed based basic modes. Furthermore, a Simulink-SimDriveline hybrid vehicle model was built for the new transmission, driveline and vehicle modular. Control strategy has also been built to harmonically coordinate different powertrain components to realize TGF function. A vehicle launch simulation test has been completed under 30% of accelerator pedal position to reveal details during gearshift. Simulation results showed that this HAMT can eliminate most torque gap that has been persistent issue of traditional AMT, improving both drivability and performance. This work demonstrated a new type of transmission that features high torque capacity, high efficiency and improved drivability.
Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms
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...
Energy Technology Data Exchange (ETDEWEB)
Xu Yuhua [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teacher' s College, Hubei 442000 (China)], E-mail: yuhuaxu2004@163.com; Zhou Wuneng [College of Information Science and Technology, Donghua University, Shanghai 201620 (China)], E-mail: wnzhou@163.com; Fang Jianan [College of Information Science and Technology, Donghua University, Shanghai 201620 (China)
2009-11-15
This paper introduces a modified Lue chaotic system, and some basic dynamical properties are studied. Based on these properties, we present hybrid dislocated control method for stabilizing chaos to unstable equilibrium and limit cycle. In addition, based on the Lyapunov stability theorem, general hybrid projective dislocated synchronization (GHPDS) is proposed, which includes complete dislocated synchronization, dislocated anti-synchronization and projective dislocated synchronization as its special item. The drive and response systems discussed in this paper can be strictly different dynamical systems (including different dimensional systems). As examples, the modified Lue chaotic system, Chen chaotic system and hyperchaotic Chen system are discussed. Numerical simulations are given to show the effectiveness of these methods.
Filtering and control of stochastic jump hybrid systems
Yao, Xiuming; Zheng, Wei Xing
2016-01-01
This book presents recent research work on stochastic jump hybrid systems. Specifically, the considered stochastic jump hybrid systems include Markovian jump Ito stochastic systems, Markovian jump linear-parameter-varying (LPV) systems, Markovian jump singular systems, Markovian jump two-dimensional (2-D) systems, and Markovian jump repeated scalar nonlinear systems. Some sufficient conditions are first established respectively for the stability and performances of those kinds of stochastic jump hybrid systems in terms of solution of linear matrix inequalities (LMIs). Based on the derived analysis conditions, the filtering and control problems are addressed. The book presents up-to-date research developments and novel methodologies on stochastic jump hybrid systems. The contents can be divided into two parts: the first part is focused on robust filter design problem, while the second part is put the emphasis on robust control problem. These methodologies provide a framework for stability and performance analy...
Kanzaki, Ryohei; Minegishi, Ryo; Namiki, Shigehiro; Ando, Noriyasu
2013-11-01
To elucidate the dynamic information processing in a brain underlying adaptive behavior, it is necessary to understand the behavior and corresponding neural activities. This requires animals which have clear relationships between behavior and corresponding neural activities. Insects are precisely such animals and one of the adaptive behaviors of insects is high-accuracy odor source orientation. The most direct way to know the relationships between neural activity and behavior is by recording neural activities in a brain from freely behaving insects. There is also a method to give stimuli mimicking the natural environment to tethered insects allowing insects to walk or fly at the same position. In addition to these methods an 'insect-machine hybrid system' is proposed, which is another experimental system meeting the conditions necessary for approaching the dynamic processing in the brain of insects for generating adaptive behavior. This insect-machine hybrid system is an experimental system which has a mobile robot as its body. The robot is controlled by the insect through its behavior or the neural activities recorded from the brain. As we can arbitrarily control the motor output of the robot, we can intervene at the relationship between the insect and the environmental conditions.
Energy Technology Data Exchange (ETDEWEB)
Salcedo-Sanz, Sancho; Perez-Bellido, Angel M.; Ortiz-Garcia, Emilio G.; Portilla-Figueras, Antonio [Department of Signal Theory and Communications, Universidad de Alcala, Madrid (Spain); Prieto, Luis [Wind Resource Department, Iberdrola Renovables, Madrid (Spain); Paredes, Daniel [Department of Physics of the Earth, Astronomy and Astrophysics II, Universidad Complutense de Madrid (Spain)
2009-06-15
This paper presents the hybridization of the fifth generation mesoscale model (MM5) with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at wind turbines in a wind park is an important parameter used to predict the total power production of the park. Our model for short-term wind speed forecast integrates a global numerical weather prediction model and observations at different heights (using atmospheric soundings) as initial and boundary conditions for the MM5 model. Then, the outputs of this model are processed using a neural network to obtain the wind speed forecast in specific points of the wind park. In the experiments carried out, we present some results of wind speed forecasting in a wind park located at the south-east of Spain. The results are encouraging, and show that our hybrid MM5-neural network approach is able to obtain good short-term predictions of wind speed at specific points. (author)
Glass, J O; Reddick, W E
1998-11-01
The evaluation of pediatric osteosarcoma has suffered from the lack of an accurate imaging measure of response. One major problem is that osteosarcoma do not shrink in response to chemotherapy; instead, viable tumor is replaced by necrotic tissue. Currently available techniques that use dynamic contrast-enhanced magnetic resonance imaging to quantitatively evaluate tumor response fail to assess the percentage of necrosis. At present, histopathologic evaluation of resected tissue is the only means of measuring the percentage of necrosis in treated osteosarcoma. The current study presents a non-invasive method to visualize necrotic and viable tumor and quantitatively assess the response of osteosarcoma. Our technique uses a hybrid neural network consisting of a Kohonen self-organizing map to segment dynamic contrast-enhanced magnetic resonance images and a multi-layer backpropagation neural network to classify the segmented images. Because the hybrid neural network is completely automated, our technique removes both inter- and intra-operator error. An analysis comparing the percentage of necrosis from our technique to the histopathologic analysis revealed a highly significant Spearman correlation coefficient of 0.617 with p < 0.001.
Nuclear Hybrid energy Systems: Molten Salt Energy Storage
Energy Technology Data Exchange (ETDEWEB)
Green, M.; Sabharwall, P.; Yoon, S. J.; Bragg-Sitton, S. B.; Stoot, C.
2014-07-01
Without growing concerns in reliable energy supply, the next generation in reliable power generation via hybrid energy systems is being developed. A hybrid energy system incorporates multiple energy input source sand multiple energy outputs. The vitality and efficiency of these combined systems resides in the energy storage application. Energy storage is necessary for grid stabilization because stored excess energy is used later to meet peak energy demands. With high thermal energy production the primary nuclear heat generation source, molten salt energy storage is an intriguing option because of its distinct thermal properties. This paper discusses the criteria for efficient energy storage and molten salt energy storage system options for hybrid systems. (Author)
Modeling and Analysis of Hybrid Dynamic Systems Using Hybrid Petri Nets
GHOMRI Latefa; Alla, Hassane
2008-01-01
Some extensions of PNs permitting HDS modeling were presented here. The first models to be presented are continuous PNs. This model may be used for modeling either a continuous system or a discrete system. In this case, it is an approximation that is often satisfactory. Hybrid PNs combine in the same formalism a discrete PN and a continuous PN. Two hybrid PN models were considered in this chapter. The first, called the hybrid PN, has a deterministic behavior; this means that we can predict th...
Neural Network Predictive Control Based Power System Stabilizer
Directory of Open Access Journals (Sweden)
Ali Mohamed Yousef
2012-04-01
Full Text Available The present study investigates the power system stabilizer based on neural predictive control for improving power system dynamic performance over a wide range of operating conditions. In this study a design and application of the Neural Network Model Predictive Controller (NN-MPC on a simple power system composed of a synchronous generator connected to an infinite bus through a transmission line is proposed. The synchronous machine is represented in detail, taking into account the effect of the machine saliency and the damper winding. Neural network model predictive control combines reliable prediction of neural network model with excellent performance of model predictive control using nonlinear Levenberg-Marquardt optimization. This control system is used the rotor speed deviation as a feedback signal. Furthermore, the using performance system of the proposed controller is compared with the system performance using conventional one (PID controller through simulation studies. Digital simulation has been carried out in order to validate the effectiveness proposed NN-MPC power system stabilizer for achieving excellent performance. The results demonstrate that the effectiveness and superiority of the proposed controller in terms of fast response and small settling time.
Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine
Energy Technology Data Exchange (ETDEWEB)
Ondrej Linda; Milos Manic; Timothy R. McJunkin
2011-08-01
Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.
Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine
Energy Technology Data Exchange (ETDEWEB)
Ondrej Linda; Milos Manic; Timothy R. McJunkin
2011-08-01
Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.
Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System
DEFF Research Database (Denmark)
Lehmann, Torsten
1998-01-01
In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop...... a suitable learning algorithm -- a continuous-time version of a temporal differential Hebbian learning algorithm for pulsed neural systems with non-linear synapses -- as well as circuits for the electronic implementation. Measurements from an experimental CMOS chip are presented. Finally, we use our test...
FUZZY NEURAL NETWORK FOR MACHINE PARTS RECOGNITION SYSTEM
Institute of Scientific and Technical Information of China (English)
Luo Xiaobin; Yin Guofu; Chen Ke; Hu Xiaobing; Luo Yang
2003-01-01
The primary purpose is to develop a robust adaptive machine parts recognition system. A fuzzy neural network classifier is proposed for machine parts classifier. It is an efficient modeling method. Through learning, it can approach a random nonlinear function. A fuzzy neural network classifier is presented based on fuzzy mapping model. It is used for machine parts classification. The experimental system of machine parts classification is introduced. A robust least square back-propagation (RLSBP) training algorithm which combines robust least square (RLS) with back-propagation (BP) algorithm is put forward. Simulation and experimental results show that the learning property of RLSBP is superior to BP.
Adaptive Synchronization of Memristor-based Chaotic Neural Systems
Directory of Open Access Journals (Sweden)
Xiaofang Hu
2014-11-01
Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.
Advantages of Hybrid Global Navigation Satellite Systems
Directory of Open Access Journals (Sweden)
Asim Bilajbegović
2007-05-01
Full Text Available In a decision-making situation, what kind of GPS equipment to purchase, one always has a dilemma, tobuy hybrid (GPS+GLONASS or only GPS receivers? In the case of completeness of the GLONASS satellite system, this dilemma probably would not have existed. The answer to this dilemma is given in the present paper, but for the constellation of the GLONASS satellites in summer 2006 (14 satellites operational. Due to the short operational period of these satellites (for example GLONASS-M, 5 years, and not launching new ones, at this moment (February 25, 2007, only 10 satellites are operational. For the sake of research and giving answers to these questions, about 252 RTK measurements have been done using (GPS and GNSS receivers, on points with different obstructions of horizon. Besides that, initialisation time has been investigated for both systems from about 480 measurements, using rover's antenna with metal cover, during a time interval of 0.5, 2 and 5 seconds. Moreover, accuracy, firmware declared accuracy and redundancy of GPS and GNSS RTK measurements have been investigating.
Review of hybrid laminar flow control systems
Krishnan, K. S. G.; Bertram, O.; Seibel, O.
2017-08-01
The aeronautic community always strived for fuel efficient aircraft and presently, the need for ecofriendly aircraft is even more, especially with the tremendous growth of air traffic and growing environmental concerns. Some of the important drivers for such interests include high fuel prices, less emissions requirements, need for more environment friendly aircraft to lessen the global warming effects. Hybrid laminar flow control (HLFC) technology is promising and offers possibility to achieve these goals. This technology was researched for decades for its application in transport aircraft, and it has achieved a new level of maturity towards integration and safety and maintenance aspects. This paper aims to give an overview of HLFC systems research and associated flight tests in the past years both in the US and in Europe. The review makes it possible to distinguish between the successful approaches and the less successful or outdated approaches in HLFC research. Furthermore, the technology status shall try to produce first estimations regarding the mass, power consumption and performance of HLFC systems as well as estimations regarding maintenance requirements and possible subsystem definitions.
Energy Technology Data Exchange (ETDEWEB)
Surber, F.T.
1979-09-30
The results of investigations conducted under Ce Hybrid Vehicle Potential Assessment Task are reported in 10 volumes. This volume contains an overview of the study and its results. The purpose of the overall study was to determine if the petroleum fuel savings achievable through the use of hybrid electric vehicles is worth the R and D expenditures needed to develop the hybrid vehicles and to determine R and D priorities. It was concluded that by the year 2010 hybrid vehicles could replace 80% of the automotive power that would otherwise be produced from petroleum fuels; the public should not suffer any mobility loss through the use of hybrid vehicles; high initial and life-cycle costs are a limiting factor; and R and D funds should be spent for systems design and the development of low-cost batteries and controllers. (LCL)
Reliability Modeling of Microelectromechanical Systems Using Neural Networks
Perera. J. Sebastian
2000-01-01
Microelectromechanical systems (MEMS) are a broad and rapidly expanding field that is currently receiving a great deal of attention because of the potential to significantly improve the ability to sense, analyze, and control a variety of processes, such as heating and ventilation systems, automobiles, medicine, aeronautical flight, military surveillance, weather forecasting, and space exploration. MEMS are very small and are a blend of electrical and mechanical components, with electrical and mechanical systems on one chip. This research establishes reliability estimation and prediction for MEMS devices at the conceptual design phase using neural networks. At the conceptual design phase, before devices are built and tested, traditional methods of quantifying reliability are inadequate because the device is not in existence and cannot be tested to establish the reliability distributions. A novel approach using neural networks is created to predict the overall reliability of a MEMS device based on its components and each component's attributes. The methodology begins with collecting attribute data (fabrication process, physical specifications, operating environment, property characteristics, packaging, etc.) and reliability data for many types of microengines. The data are partitioned into training data (the majority) and validation data (the remainder). A neural network is applied to the training data (both attribute and reliability); the attributes become the system inputs and reliability data (cycles to failure), the system output. After the neural network is trained with sufficient data. the validation data are used to verify the neural networks provided accurate reliability estimates. Now, the reliability of a new proposed MEMS device can be estimated by using the appropriate trained neural networks developed in this work.
Humphrey, Greer B.; Gibbs, Matthew S.; Dandy, Graeme C.; Maier, Holger R.
2016-09-01
Monthly streamflow forecasts are needed to support water resources decision making in the South East of South Australia, where baseflow represents a significant proportion of the total streamflow and soil moisture and groundwater are important predictors of runoff. To address this requirement, the utility of a hybrid monthly streamflow forecasting approach is explored, whereby simulated soil moisture from the GR4J conceptual rainfall-runoff model is used to represent initial catchment conditions in a Bayesian artificial neural network (ANN) statistical forecasting model. To assess the performance of this hybrid forecasting method, a comparison is undertaken of the relative performances of the Bayesian ANN, the GR4J conceptual model and the hybrid streamflow forecasting approach for producing 1-month ahead streamflow forecasts at three key locations in the South East of South Australia. Particular attention is paid to the quantification of uncertainty in each of the forecast models and the potential for reducing forecast uncertainty by using the hybrid approach is considered. Case study results suggest that the hybrid models developed in this study are able to take advantage of the complementary strengths of both the ANN models and the GR4J conceptual models. This was particularly the case when forecasting high flows, where the hybrid models were shown to outperform the two individual modelling approaches in terms of the accuracy of the median forecasts, as well as reliability and resolution of the forecast distributions. In addition, the forecast distributions generated by the hybrid models were up to 8 times more precise than those based on climatology; thus, providing a significant improvement on the information currently available to decision makers.
Improving Diagnosability of Hybrid Systems through Active Diagnosis
National Aeronautics and Space Administration — Fault diagnosis is key to ensuring system safety through fault-adaptive control. This task is diffcult in hybrid systems with combined continuous and discrete...
Comparison of metallization systems for thin film hybrid microcircuits
Energy Technology Data Exchange (ETDEWEB)
Hines, R.A.; Raut, M.K.
1980-08-01
Five metallization systems were evaluated for fabricating thin film hybrid microcircuits. The titanium/palladium/electroplated gold system proved superior in terms of thermocompression bondability, corrosion resistance, and solderability.
ANN based optimization of a solar assisted hybrid cooling system in Turkey
Energy Technology Data Exchange (ETDEWEB)
Ozgur, Arif; Yetik, Ozge; Arslan, Oguz [Mechanical Eng. Dept., Engineering Faculty, Dumlupinar University (Turkey)], email: maozgur@dpu.edu.tr, email: ozgeyetik@dpu.edu.tr, email: oarslan@dpu.edu.tr
2011-07-01
This study achieved optimization of a solar assisted hybrid cooling system with refrigerants such as R717, R141b, R134a and R123 using an artificial neural network (ANN) model based on average total solar radiation, ambient temperature, generator temperature, condenser temperature, intercooler temperature and fluid types. ANN is a new tool; it works rapidly and can thus be a solution for design and optimization of complex power cycles. A unique flexible ANN algorithm was introduced to evaluate the solar ejector cooling systems because of the nonlinearity of neural networks. The conclusion was that the best COPs value obtained with the ANN is 1.35 and COPc is 3.03 when the average total solar radiation, ambient temperature, generator temperature, condenser temperature, intercooler temperature and algorithm are respectively 674.72 W/m2, 17.9, 80, 15 and 13 degree celsius and LM with 14 neurons in single hidden layer, for R717.
Identification and estimation algorithm for stochastic neural system. II.
Nakao, M; Hara, K; Kimura, M; Sato, R
1985-01-01
The algorithm for identifying the stochastic neural system and estimating the system process which reflects the dynamics of the neural network are presented in this paper. The analogous algorithm has been proposed in our preceding paper (Nakao et al., 1984), which was based on the randomly missed observations of a system process only. Since the previous algorithm mentioned above was subject to an unfavorable effect of consecutively missed observations, to reduce such an effect the algorithm proposed here is designed additionally to observe an intensity process in a neural spike train as the information for the estimation. The algorithm is constructed with the extended Kalman filters because it is naturally expected that a nonlinear and time variant structure is necessary for the filters to realize the observation of an intensity process by means of mapping from a system process to an intensity process. The performance of the algorithm is examined by applying it to some artificial neural systems and also to cat's visual nervous systems. The results in these applications are thought to prove the effectiveness of the algorithm proposed here and its superiority to the algorithm proposed previously.
Approximation Problems in System Identification With Neural Networks
Institute of Scientific and Technical Information of China (English)
陈天平
1994-01-01
In this paper, the capability of neural networks and some approximation problens in system identification with neural networks are investigated. Some results are given: (i) For any function g ∈Llocp (R1) ∩S’ (R1) to be an Lp-Tauber-Wiener function, it is necessary and sufficient that g is not apolynomial; (ii) If g∈(Lp TW), then the set of is dense in Lp(K)’ (iii) It is proved that bycompositions of some functions of one variable, one can approximate continuous functional defined on compact Lp(K) and continuous operators from compact Lp1(K1) to LP2(K2). These results confirm the capability of neural networks in identifying dynamic systems.
Application of dynamic recurrent neural networks in nonlinear system identification
Du, Yun; Wu, Xueli; Sun, Huiqin; Zhang, Suying; Tian, Qiang
2006-11-01
An adaptive identification method of simple dynamic recurrent neural network (SRNN) for nonlinear dynamic systems is presented in this paper. This method based on the theory that by using the inner-states feed-back of dynamic network to describe the nonlinear kinetic characteristics of system can reflect the dynamic characteristics more directly, deduces the recursive prediction error (RPE) learning algorithm of SRNN, and improves the algorithm by studying topological structure on recursion layer without the weight values. The simulation results indicate that this kind of neural network can be used in real-time control, due to its less weight values, simpler learning algorithm, higher identification speed, and higher precision of model. It solves the problems of intricate in training algorithm and slow rate in convergence caused by the complicate topological structure in usual dynamic recurrent neural network.
Nuclear Hybrid Energy System Modeling: RELAP5 Dynamic Coupling Capabilities
Energy Technology Data Exchange (ETDEWEB)
Piyush Sabharwall; Nolan Anderson; Haihua Zhao; Shannon Bragg-Sitton; George Mesina
2012-09-01
The nuclear hybrid energy systems (NHES) research team is currently developing a dynamic simulation of an integrated hybrid energy system. A detailed simulation of proposed NHES architectures will allow initial computational demonstration of a tightly coupled NHES to identify key reactor subsystem requirements, identify candidate reactor technologies for a hybrid system, and identify key challenges to operation of the coupled system. This work will provide a baseline for later coupling of design-specific reactor models through industry collaboration. The modeling capability addressed in this report focuses on the reactor subsystem simulation.
Bond graph model-based fault diagnosis of hybrid systems
Borutzky, Wolfgang
2015-01-01
This book presents a bond graph model-based approach to fault diagnosis in mechatronic systems appropriately represented by a hybrid model. The book begins by giving a survey of the fundamentals of fault diagnosis and failure prognosis, then recalls state-of-art developments referring to latest publications, and goes on to discuss various bond graph representations of hybrid system models, equations formulation for switched systems, and simulation of their dynamic behavior. The structured text: • focuses on bond graph model-based fault detection and isolation in hybrid systems; • addresses isolation of multiple parametric faults in hybrid systems; • considers system mode identification; • provides a number of elaborated case studies that consider fault scenarios for switched power electronic systems commonly used in a variety of applications; and • indicates that bond graph modelling can also be used for failure prognosis. In order to facilitate the understanding of fault diagnosis and the presented...
Hybrid Recommender System for Joining Virtual Communities
Directory of Open Access Journals (Sweden)
Leila Esmaeili
2012-03-01
Full Text Available The variety of social networks and virtual communities has created problematic for users of different ages and preferences; in addition, since the true nature of groups is not clearly outlined, users are uncertain about joining various virtual groups and usually face the trouble of joining the undesired ones. As a solution, in this study, we introduced the hybrid community recommender system which offers customized recommendations based on user preferences. Although techniques such as content based filtering and collaborative filtering methods are available, these techniques are not enough efficient and in some cases make problems and bring limitations to users. Our method is based on a combination of content based filtering and collaborative filtering methods. It is created by selecting related features of users based on supervised entropy as well as using association rules and classification method. Supposing users in each community or group share similar characteristics, by hierarchical clustering, heterogeneous members are identified and removed. Unlike other methods, this is also applicable for users who have just joined the social network where they do not have any connections or group memberships. In such situations, this method could still offer recommendations.
Polymer hybrid materials for planar optronic systems
Körner, Martin; Prucker, Oswald; Rühe, Jürgen
2015-09-01
Planar optronic systems made entirely from polymeric functional materials on polymeric foils are interesting architectures for monitoring and sensing applications. Key components in this regard are polymer hybrid materials with adjustable optical properties. These materials can then be processed into optical components such as waveguides for example by using embossing techniques. However, the resulting microstructures have often low mechanical or thermal stability which quickly leads to a degradation of the microstructures accompanied often by a complete loss of function. A simple and versatile way to increase the thermal and mechanical stability of polymers is to connect the individual chains to a polymer network by using thermally or photochemically reactive groups. Upon excitation, these groups form reactive intermediates such as radicals or nitrenes which then crosslink with adjacent C-H-groups through a C,H insertion reaction (CHic = C,H insertion based crosslinking). To generate waveguide structures a PDMS stamp is filled with the waveguide core material e.g. poly(methylmethacrylate) (PMMA), which is modified with a few mol% of the thermal crosslinker and hot embossed onto a foil substrate e.g. PMMA. In this one-step hot embossing process polymer ridge waveguides are formed and simultaneously the polymer becomes crosslinked. Due to the reaction across the boundary between waveguide and substrate it is also possible to combine initially incompatible polymers for the waveguide and the substrate foil. The thermomechanical properties of the obtained materials are studied.
Salcedo-Sanz, Sancho; Santiago-Mozos, Ricardo; Bousoño-Calzón, Carlos
2004-04-01
A hybrid Hopfield network-simulated annealing algorithm (HopSA) is presented for the frequency assignment problem (FAP) in satellite communications. The goal of this NP-complete problem is minimizing the cochannel interference between satellite communication systems by rearranging the frequency assignment, for the systems can accommodate the increasing demands. The HopSA algorithm consists of a fast digital Hopfield neural network which manages the problem constraints hybridized with a simulated annealing which improves the quality of the solutions obtained. We analyze the problem and its formulation, describing and discussing the HopSA algorithm and solving a set of benchmark problems. The results obtained are compared with other existing approaches in order to show the performance of the HopSA approach.
Expert,Neural and Fuzzy Systems in Process Planning
Institute of Scientific and Technical Information of China (English)
无
1999-01-01
Computer aided process planning (CAPP) aims at improving efficiency, quali t y, and productivity in a manufacturing concern through reducing lead-times and costs by utilizing better manufacturing practices thus improving competitiveness in the market. CAPP attempts to capture the thoughts and methods of the experie nced process planner. Variant systems are understandable, generative systems can plan new parts. Expert systems increase flexibility, fuzzy logic captures vague knowledge while neural networks learn. The combination of fuzzy, neural and exp ert system technologies is necessary to capture and utilize the process planning logic. A system that maintains the dependability and clarity of variant systems , is capable of planning new parts, and improves itself through learning is neede d by industry.
Engineering neural systems for high-level problem solving.
Sylvester, Jared; Reggia, James
2016-07-01
There is a long-standing, sometimes contentious debate in AI concerning the relative merits of a symbolic, top-down approach vs. a neural, bottom-up approach to engineering intelligent machine behaviors. While neurocomputational methods excel at lower-level cognitive tasks (incremental learning for pattern classification, low-level sensorimotor control, fault tolerance and processing of noisy data, etc.), they are largely non-competitive with top-down symbolic methods for tasks involving high-level cognitive problem solving (goal-directed reasoning, metacognition, planning, etc.). Here we take a step towards addressing this limitation by developing a purely neural framework named galis. Our goal in this work is to integrate top-down (non-symbolic) control of a neural network system with more traditional bottom-up neural computations. galis is based on attractor networks that can be "programmed" with temporal sequences of hand-crafted instructions that control problem solving by gating the activity retention of, communication between, and learning done by other neural networks. We demonstrate the effectiveness of this approach by showing that it can be applied successfully to solve sequential card matching problems, using both human performance and a top-down symbolic algorithm as experimental controls. Solving this kind of problem makes use of top-down attention control and the binding together of visual features in ways that are easy for symbolic AI systems but not for neural networks to achieve. Our model can not only be instructed on how to solve card matching problems successfully, but its performance also qualitatively (and sometimes quantitatively) matches the performance of both human subjects that we had perform the same task and the top-down symbolic algorithm that we used as an experimental control. We conclude that the core principles underlying the galis framework provide a promising approach to engineering purely neurocomputational systems for problem
Energy Technology Data Exchange (ETDEWEB)
Pérez, E. [Institut Ciència de Materials de Barcelona (ICMAB-CSIC), Campus UAB, E-08193, Bellaterra, Barcelona (Spain); Lichtenstein, M.P.; Suñol, C. [Institut d' Investigacions Biomèdiques de Barcelona (IIBB-CSIC), Institut d' Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c/Rosselló 161, 08036 Barcelona (Spain); Casañ-Pastor, N., E-mail: nieves@icmab.es [Institut Ciència de Materials de Barcelona (ICMAB-CSIC), Campus UAB, E-08193, Bellaterra, Barcelona (Spain)
2015-10-01
The need to enhance charge capacity in neural stimulation-electrodes is promoting the formation of new materials and coatings. Among all the possible types of graphene, pristine graphene prepared by graphite electrochemical exfoliation, is used in this work to form a new nanostructured IrOx–graphene hybrid (IrOx–eG). Graphene is stabilized in suspension by IrOx nanoparticles without surfactants. Anodic electrodeposition results in coatings with much smaller roughness than IrOx–graphene oxide. Exfoliated pristine graphene (eG), does not electrodeposit in absence of iridium, but IrOx-nanoparticle adhesion on graphene flakes drives the process. IrOx–eG has a significantly different electronic state than graphene oxide, and different coordination for carbon. Electron diffraction shows the reflection features expected for graphene. IrOx 1–2 nm cluster/nanoparticles are oxohydroxo-species and adhere to 10 nm graphene platelets. eG induces charge storage capacity values five times larger than in pure IrOx, and if calculated per carbon atom, this enhancement is one order magnitude larger than the induced by graphene oxide. IrOx–eG coatings show optimal in vitro neural cell viability and function as cell culture substrates. The fully straightforward electrochemical exfoliation and electrodeposition constitutes a step towards the application of graphene in biomedical systems, expanding the knowledge of pristine graphene vs. graphene oxide, in bioelectrodes. - Highlights: • Pristine Graphene is incorporated in coatings as nanostructured IrOx–eG hybrid. • IrOx-nanoparticles drive the electrodeposition of graphene. • Hybrid CSC is one order of magnitude the charge capacity of IrOx. • Per carbon atom, the CSC increase is 35 times larger than for graphene oxide. • Neurons are fully functional on the coating.
Gan, Ruijing; Chen, Xiaojun; Yan, Yu; Huang, Daizheng
2015-01-01
Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method's feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.
Directory of Open Access Journals (Sweden)
Haisheng Song
2013-01-01
Full Text Available The back propagation neural network (BPNN algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.
Directory of Open Access Journals (Sweden)
Rupinder Singh
2017-01-01
Full Text Available In this paper, an Advanced Hybrid Intrusion Detection System (AHIDS that automatically detects the WSNs attacks is proposed. AHIDS makes use of cluster-based architecture with enhanced LEACH protocol that intends to reduce the level of energy consumption by the sensor nodes. AHIDS uses anomaly detection and misuse detection based on fuzzy rule sets along with the Multilayer Perceptron Neural Network. The Feed Forward Neural Network along with the Backpropagation Neural Network are utilized to integrate the detection results and indicate the different types of attackers (i.e., Sybil attack, wormhole attack, and hello flood attack. For detection of Sybil attack, Advanced Sybil Attack Detection Algorithm is developed while the detection of wormhole attack is done by Wormhole Resistant Hybrid Technique. The detection of hello flood attack is done by using signal strength and distance. An experimental analysis is carried out in a set of nodes; 13.33% of the nodes are determined as misbehaving nodes, which classified attackers along with a detection rate of the true positive rate and false positive rate. Sybil attack is detected at a rate of 99,40%; hello flood attack has a detection rate of 98, 20%; and wormhole attack has a detection rate of 99, 20%.
Periodic orbits of hybrid systems and parameter estimation via AD.
Energy Technology Data Exchange (ETDEWEB)
Guckenheimer, John. (Cornell University); Phipps, Eric Todd; Casey, Richard (INRIA Sophia-Antipolis)
2004-07-01
a definition of hybrid systems that is the basis for modeling systems with discontinuities or discrete transitions. Sections 2, 3, and 4 briefly describe the Taylor series integration, periodic orbit tracking, and parameter estimation algorithms. For full treatments of these algorithms, we refer the reader to [Phi03, Cas04, CPG04]. The software implementation of these algorithms is briefly described in Section 5 with particular emphasis on the automatic differentiation software ADMC++. Finally, these algorithms are applied to the bipedal walking and Hodgkin-Huxley based neural oscillation problems discussed above in Section 6.
Battery control system for hybrid vehicle and method for controlling a hybrid vehicle battery
Bockelmann, Thomas R [Battle Creek, MI; Hope, Mark E [Marshall, MI; Zou, Zhanjiang [Battle Creek, MI; Kang, Xiaosong [Battle Creek, MI
2009-02-10
A battery control system for hybrid vehicle includes a hybrid powertrain battery, a vehicle accessory battery, and a prime mover driven generator adapted to charge the vehicle accessory battery. A detecting arrangement is configured to monitor the vehicle accessory battery's state of charge. A controller is configured to activate the prime mover to drive the generator and recharge the vehicle accessory battery in response to the vehicle accessory battery's state of charge falling below a first predetermined level, or transfer electrical power from the hybrid powertrain battery to the vehicle accessory battery in response to the vehicle accessory battery's state of charge falling below a second predetermined level. The invention further includes a method for controlling a hybrid vehicle powertrain system.
A rule-based neural controller for inverted pendulum system.
Hao, J; Vandewalle, J; Tan, S
1993-03-01
This paper tries to demonstrate how a heuristic neural control approach can be used to solve a complex nonlinear control problem. The control task is to swing up a pendulum mounted on a cart from its stable position (vertically down) to the zero state (up right) and keep it there by applying a sequence of two opposing constant forces of equal magnitude to the mass center of the cart. In addition, the displacement of the cart itself is confined to within a preset limit during the swinging up action and it will eventually be brought to the origin of the track. This is truly a nontrivial nonlinear regulation problem and is considerably difficult compared to the pendulum balancing problem (and its variations) widely adopted as a benchmarking test system for neural controllers. Through the solution of this specific control problem, we try to illustrate a heuristic neural control approach with task decomposition, control rule extraction and neural net rule implementation as its basic elements. Specializing to the pendulum problem, the global control task is decomposed into subtasks namely pendulum positioning and cart positioning. Accordingly, three separate neural subcontrollers are designed to cater to the subtasks and their coordination, i.e., pendulum subcontroller (PSC), cart subcontroller (CSC) and the switching subcontroller (SSC). Each of the subcontrollers is designed based on the rules and guidelines obtained from the experiences of a human operator. The simulation result is included to show the actual performance of the controller.
Investigation of the photovoltaic cell/ thermoelectric element hybrid system performance
Cotfas, D. T.; Cotfas, P. A.; Machidon, O. M.; Ciobanu, D.
2016-06-01
The PV/TEG hybrid system, consisting of the photovoltaic cells and thermoelectric element, is presented in the paper. The dependence of the PV/TEG hybrid system parameters on the illumination levels and the temperature is analysed. The maxim power values of the photovoltaic cell, of the thermoelectric element and of the PV/TEG system are calculated and a comparison between them is presented and analysed. An economic analysis is also presented.
A simulation approach to sizing hybrid photovoltaic and wind systems
Anderson, L. A.
1983-12-01
A simulation approach to sizing hybrid photovoltaic and wind systems provides a combination of components to realize zero downtime and minimum initial or life-cycle cost. Using Dayton, OH as a test site for weather data, cost advantages in the neighborhood of four are predicted for a hybrid system with battery storage when compared to a wind-energy-only system for the same electrical load.
Sizing and Simulation of PV-Wind Hybrid Power System
Mustafa Engin
2013-01-01
A sizing procedure is developed for hybrid system with the aid of mathematical models for photovoltaic cell, wind turbine, and battery that are readily present in the literature. This sizing procedure can simulate the annual performance of different kinds of photovoltaic-wind hybrid power system structures for an identified set of renewable resources, which fulfills technical limitations with the lowest energy cost. The output of the program will display the performance of the system during t...
Directory of Open Access Journals (Sweden)
Yuehjen E. Shao
2013-01-01
Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.
Tóth-Nagy, Csaba; Conley, John J; Jarrett, Ronald P; Clark, Nigel N
2006-07-01
With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO2] and oxides of nitrogen [NO(x)]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO2 and NO(x) in the case of a class-8 truck but were more accurate as the truck weight increased.
Aspherical lens design using hybrid neural-genetic algorithm of contact lenses.
Yen, Chih-Ta; Ye, Jhe-Wen
2015-10-01
The design of complex contact lenses involves numerous uncertain variables. How to help an optical designer to first design the optimal contact lens to reduce discomfort when wearing a pair of glasses is an essential design concern. This study examined the impact of aberrations on contact lenses to optimize a contact lens design for myopic and astigmatic eyes. In general, two aspherical surfaces can be assembled in an optical system to reduce the overall volume size. However, this design reduces the spherical aberration (SA) values at wide contact radii. The proposed optimization algorithm with optical design can be corrected to improve the SA value and, thus, reduce coma aberration (TCO) values and enhance the modulation transfer function (MTF). This means integrating a modified genetic algorithm (GA) with a neural network (NN) to optimize multiple-quality characteristics, namely the SA, TCO, and MTF, of contact lenses. When the proposed optional weight NN-GA is implemented, the weight values of the fitness function can be varied to adjust system performance. The method simplifies the selection of parameters in the optimization of optical systems. Compared with the traditional CODE V built-in optimal scheme, the proposed scheme is more flexible and intuitive to improve SA, TCO, and MTF values by 50.03%, 45.78%, and 24.7%, respectively.
Neural transduction in Xenopus laevis lateral line system.
Strelioff, D; Honrubia, V
1978-03-01
1. The process of neural excitation in hair cell systems was studied in an in vitro preparation of the Xenopus laevis (African clawed toad) lateral line organ. A specially designed stimulus chamber was used to apply accurately controlled pressure, water movement, or electrical stimuli, and to record the neural responses of the two afferent fibers innervating each organ or stitch. The objective of the study was to determine the characteristics of the neural responses to these stimuli, and thus gain insight into the transduction process. 2. A sustained deflection of the hair cell cilia due to a constant flow of water past the capula resulted in a maintained change in the mean firing rate (MFR) of the afferent fibers. The data also demonstrated that the neural response was proportional to the velocity of the water flow and indicated that both deflection and movement of the cilia were the effective physiological stimuli for this hair cell system. 3. The preparations responded to sinusoidal water movements (past the capula) over the entire frequency range of the stimulus chamber, 0.1-130 Hz, and were most sensitive between 10 and 40 Hz. The variation of the MFR and the percent modulation indicated that the average dynamic range of each organ was 23.5 dB. 4. The thresholds, if any, for sustained pressure changes and for sinusoidal pressure variations in the absence of water movements were very high. Due to the limitations of the stimulus chamber it was not possible to generate pressure stimuli of sufficient magnitude to elicit a neural response without also generating suprathreshold water-movement stimuli. Sustained pressures had no detectable effect on the neural response to water-movement stimuli. 5. The preparations were very sensitive to electrical potentials applied across the toad skin on which the hair cells were located. Potentials which made the ciliated surfaces of the hair cells positive with respect to their bases increased the MFR of the fibers, whereas
Renewable energy systems in Mexico: Installation of a hybrid system
Pate, Ronald C.
1993-05-01
Sandia has been providing technical leadership on behalf of DOE and CORECT on a working level cooperative program with Mexico on renewable energy (PROCER). As part of this effort, the Sandia Design Assistance Center (DAC) and the solar energy program staff at Instituto de Investigaciones Electricas (IIE) in Cuernavaca, Mexico, recently reached agreement on a framework for mutually beneficial technical collaboration on the monitoring and field evaluation of renewable energy systems in Mexico, particularly village-scale hybrid systems. This trip was made for the purpose of planning the details for the joint installation of a data acquisition system (DAS) on a recently completed PV/Wind/Diesel hybrid system in the village of Xcalac on the Southeast coast of the state of Quintana Roo, Mexico. The DAS installation will be made during the week of March 15, 1993. While in Mexico, discussions were also held with personnel from.the National Autonomous University of Mexico (UNAM) Solar Energy Laboratory and several private sector companies with regard to renewable energy project activities and technical and educational support needs in Mexico.
Exact Verification of Hybrid Systems Based on Bilinear SOS Representation
Yang, Zhengfeng; Lin, Wang
2012-01-01
In this paper, we address the problem of safety verification of nonlinear hybrid systems and stability analysis of nonlinear autonomous systems. A hybrid symbolic-numeric method is presented to compute exact inequality invariants of hybrid systems and exact estimates of regions of attraction of autonomous systems efficiently. Some numerical invariants of a hybrid system or an estimate of region of attraction can be obtained by solving a bilinear SOS program via PENBMI solver or iterative method, then the modified Newton refinement and rational vector recovery techniques are applied to obtain exact polynomial invariants and estimates of regions of attraction with rational coefficients. Experiments on some benchmarks are given to illustrate the efficiency of our algorithm.
Neural mechanisms of selective attention in the somatosensory system.
Gomez-Ramirez, Manuel; Hysaj, Kristjana; Niebur, Ernst
2016-09-01
Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates.
Frequency-difference-dependent stochastic resonance in neural systems
Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong
2017-08-01
Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.
Direct hydrogen fuel cell systems for hybrid vehicles
Ahluwalia, Rajesh K.; Wang, X.
Hybridizing a fuel cell system with an energy storage system offers an opportunity to improve the fuel economy of the vehicle through regenerative braking and possibly to increase the specific power and decrease the cost of the combined energy conversion and storage systems. Even in a hybrid configuration it is advantageous to operate the fuel cell system in a load-following mode and use the power from the energy storage system when the fuel cell alone cannot meet the power demand. This paper discusses an approach for designing load-following fuel cell systems for hybrid vehicles and illustrates it by applying it to pressurized, direct hydrogen, polymer-electrolyte fuel cell (PEFC) systems for a mid-size family sedan. The vehicle level requirements relative to traction power, response time, start-up time and energy conversion efficiency are used to select the important parameters for the PEFC stack, air management system, heat rejection system and the water management system.
Wind Solar Hybrid System Rectifier Stage Topology Simulation
Directory of Open Access Journals (Sweden)
Anup M. Gakare
2014-06-01
Full Text Available This paper presents power-control strategies of a grid-connected hybrid generation system with versatile power transfer. The hybrid system allows maximum utilization of freely available renewable sources like wind and photovoltaic energies. This paper presents a new system configuration of the multi input rectifier stage for a hybrid wind and photovoltaic energy system. This configuration allows the two sources to supply the load simultaneously depending on the availability of the energy sources maximum power from the sun when it is available. An adaptive MPPT algorithm with a standard perturbs and observed method will be used for the Photo Voltaic system. The main advantage of the hybrid system is to give continuous power supply to the load. The gating pulses to the inverter switches are implemented with conventional and fuzzy controller. This hybrid wind-photo voltaic system is modeled in MATLAB/ SIMULINK environment. Simulation circuit is analyzed and results are presented for this hybrid wind and solar energy system.
Nuclear Hybrid Energy Systems FY16 Modeling Efforts at ORNL
Energy Technology Data Exchange (ETDEWEB)
Cetiner, Sacit M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Greenwood, Michael Scott [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Harrison, Thomas J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Qualls, A. L. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Guler Yigitoglu, Askin [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Fugate, David W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2016-09-01
A nuclear hybrid system uses a nuclear reactor as the basic power generation unit. The power generated by the nuclear reactor is utilized by one or more power customers as either thermal power, electrical power, or both. In general, a nuclear hybrid system will couple the nuclear reactor to at least one thermal power user in addition to the power conversion system. The definition and architecture of a particular nuclear hybrid system is flexible depending on local markets needs and opportunities. For example, locations in need of potable water may be best served by coupling a desalination plant to the nuclear system. Similarly, an area near oil refineries may have a need for emission free hydrogen production. A nuclear hybrid system expands the nuclear power plant from its more familiar central power station role by diversifying its immediately and directly connected customer base. The definition, design, analysis, and optimization work currently performed with respect to the nuclear hybrid systems represents the work of three national laboratories. Idaho National Laboratory (INL) is the lead lab working with Argonne National Laboratory (ANL) and Oak Ridge National Laboratory. Each laboratory is providing modeling and simulation expertise for the integration of the hybrid system.
Directory of Open Access Journals (Sweden)
Ting-Chia Ou
2017-04-01
Full Text Available This paper endeavors to apply a novel intelligent damping controller (NIDC for the static synchronous compensator (STATCOM to reduce the power fluctuations, voltage support and damping in a hybrid power multi-system. In this paper, we discuss the integration of an offshore wind farm (OWF and a seashore wave power farm (SWPF via a high-voltage, alternating current (HVAC electric power transmission line that connects the STATCOM and the 12-bus hybrid power multi-system. The hybrid multi-system consists of a battery energy storage system (BESS and a micro-turbine generation (MTG. The proposed NIDC consists of a designed proportional–integral–derivative (PID linear controller, an adaptive critic network and a proposed functional link-based novel recurrent fuzzy neural network (FLNRFNN. Test results show that the proposed controller can achieve better damping characteristics and effectively stabilize the network under unstable conditions.
Robust nonlinear system identification using neural-network models.
Lu, S; Basar, T
1998-01-01
We study the problem of identification for nonlinear systems in the presence of unknown driving noise, using both feedforward multilayer neural network and radial basis function network models. Our objective is to resolve the difficulty associated with the persistency of excitation condition inherent to the standard schemes in the neural identification literature. This difficulty is circumvented here by a novel formulation and by using a new class of identification algorithms recently obtained by Didinsky et al. We show how these algorithms can be exploited to successfully identify the nonlinearity in the system using neural-network models. By embedding the original problem in one with noise-perturbed state measurements, we present a class of identifiers (under L1 and L2 cost criteria) which secure a good approximant for the system nonlinearity provided that some global optimization technique is used. In this respect, many available learning algorithms in the current neural-network literature, e.g., the backpropagation scheme and the genetic algorithms-based scheme, with slight modifications, can ensure the identification of the system nonlinearity. Subsequently, we address the same problem under a third, worst case L(infinity) criterion for an RBF modeling. We present a neural-network version of an H(infinity)-based identification algorithm from Didinsky et al and show how, along with an appropriate choice of control input to enhance excitation, under both full-state-derivative information (FSDI) and noise-perturbed full-state-information (NPFSI), it leads to satisfaction of a relevant persistency of excitation condition, and thereby to robust identification of the nonlinearity. Results from several simulation studies have been included to demonstrate the effectiveness of these algorithms.
Neural Network Control of a Magnetically Suspended Rotor System
Choi, Benjamin B.
1998-01-01
Magnetic bearings offer significant advantages because they do not come into contact with other parts during operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. At the NASA Lewis Research Center, a neural network was selected as a nonlinear controller because it generates a neural model without any detailed information regarding the internal working of the magnetic bearing system. It can be used even for systems that are too complex for an accurate system model to be derived. A feed-forward architecture with a back-propagation learning algorithm was selected because of its proven performance, accuracy, and relatively easy implementation.
A Miniaturized System for Neural Signal Acquiring and Processing
Institute of Scientific and Technical Information of China (English)
WANG Min; GAO Guang-hong; XIANG Dong-sheng; CAO Mao-yong; JIA Ai-bin; DING Lei; KONG Hui-min
2008-01-01
To collect neural activity data from awake, behaving freely animals, we develop miniaturized implantable recording system by the modern chip:Programmable System on Chip(PSoC) and through chronic electrodes in the cortex. With PSoC family member CY8C29466,the system completed operational and instrument amplifiers, filters, timers, AD convertors, and serial communication, etc. The signal processing was dealt with virtual instrument technology. All of these factors can significantly affect the price and development cycle of the project. The result showed that the system was able to record and analyze neural extrocellular discharge generated by neurons continuously for a week or more. This is very useful for the interdisciplinary research of neuroscience and information engineering technique.The circuits and architecture of the devices can be adapted for neurobiology and research with other small animals.
Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.
Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua
2016-11-14
In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.
Synthesis of recurrent neural networks for dynamical system simulation.
Trischler, Adam P; D'Eleuterio, Gabriele M T
2016-08-01
We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time.
Variable Neural Adaptive Robust Control: A Switched System Approach
Energy Technology Data Exchange (ETDEWEB)
Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.
2015-05-01
Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.
Design of Magnetic Flux Feedback Controller in Hybrid Suspension System
Directory of Open Access Journals (Sweden)
Wenqing Zhang
2013-01-01
Full Text Available Hybrid suspension system with permanent magnet and electromagnet consumes little power consumption and can realize larger suspension gap. But realizing stable suspension of hybrid magnet is a tricky problem in the suspension control sphere. Considering from this point, we take magnetic flux signal as a state variable and put this signal back to suspension control system. So we can get the hybrid suspension mathematical model based on magnetic flux signal feedback. By application of MIMO feedback linearization theory, we can further realize linearization of the hybrid suspension system. And then proportion, integral, differentiation, magnetic flux density B (PIDB controller is designed. Some hybrid suspension experiments have been done on CMS04 magnetic suspension bogie of National University of Defense Technology (NUDT in China. The experiments denote that the new hybrid suspension control algorithm based on magnetic flux signal feedback designed in this paper has more advantages than traditional position-current double cascade control algorithm. Obviously, the robustness and stability of hybrid suspension system have been enhanced.
A Structural Model Decomposition Framework for Hybrid Systems Diagnosis
Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil
2015-01-01
Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.
DESIGNING A HYBRID INTELLIGENT MINING SYSTEM FOR CREDIT RISK EVALUATION
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
In this study,a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis.In the proposed hybrid intelligent system,support vector machines are used as a tool to extract typical features and filter its noise,which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines.Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets.Therefore,the proposed hybrid intelligent system overcomes the diffi-culty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches.In addition,the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.
Asymptotic Stabilizability of a Class of Stochastic Nonlinear Hybrid Systems
Directory of Open Access Journals (Sweden)
Ewelina Seroka
2015-01-01
Full Text Available The problem of the asymptotic stabilizability in probability of a class of stochastic nonlinear control hybrid systems (with a linear dependence of the control with state dependent, Markovian, and any switching rule is considered in the paper. To solve the issue, the Lyapunov technique, including a common, single, and multiple Lyapunov function, the hybrid control theory, and some results for stochastic nonhybrid systems are used. Sufficient conditions for the asymptotic stabilizability in probability for a considered class of hybrid systems are formulated. Also the stabilizing control in a feedback form is considered. Furthermore, in the case of hybrid systems with the state dependent switching rule, a method for a construction of stabilizing switching rules is proposed. Obtained results are illustrated by examples and numerical simulations.
Evolutionary design of discrete controllers for hybrid mechatronic systems
DEFF Research Database (Denmark)
Dupuis, Jean-Francois; Fan, Zhun; Goodman, Erik
2015-01-01
This paper investigates the issue of evolutionary design of controllers for hybrid mechatronic systems. Finite State Automaton (FSA) is selected as the representation for a discrete controller due to its interpretability, fast execution speed and natural extension to a statechart, which is very...... popular in industrial applications. A case study of a two-tank system is used to demonstrate that the proposed evolutionary approach can lead to a successful design of an FSA controller for the hybrid mechatronic system, represented by a hybrid bond graph. Generalisation of the evolved FSA controller...... of the evolutionary design of controllers for hybrid mechatronic systems. Finally, some important future research directions are pointed out, leading to the major work of the succeeding part of the research....
A Novel Single Phase Hybrid Switched Reluctance Motor Drive System
DEFF Research Database (Denmark)
Liang, Jianing; Xu, Guoqing; Jian, Linni
2011-01-01
In this paper, a novel single phase hybrid switched reluctance motor(SRM) drive system is proposed. It integrated a single phase hybrid SRM and a novel single phase boost converter. This motor can reduce the number of phase switch. And the permanent magnet which is used in the motor can improve t...... SRM reduce the negative torque before zero-crossing point of torque curve, and build desired phase current to generate more power. Some experimental results are done to verify the performance of proposed hybrid SRM drive system....
Hybrid synchronization of two independent chaotic systems on complex network
Indian Academy of Sciences (India)
NIAN FUZHONG; LIU WEILONG
2016-06-01
The real network nodes are always interfered by other messages. So, how to realize the hybrid synchronization of two independent chaotic systems based on the complex network is very important. To solve this problem, two other problems should be considered. One is how the same network node of the complex network was affected by different information sources. Another is how to achieve hybrid synchronization on the network. In this paper, the theoretical analysis andnumerical simulation on various complex networks are implemented. The results indicate that the hybrid synchronization of two independent chaotic systems is feasible.
Adaptive Neural Network Controller for Thermogenerator Angular Velocity Stabilization System
2013-01-01
The paper presents an analytical and simulation approach for the selection of activation functions for the class of neural network controllers for ship’s thermogenerator angular velocity stabilization system. Such systems can be found in many ships. A Lyapunov-like stability analysis is performed in order to obtain a weight update law. A number of simulations were performed to find the best activation function using integral error criteria and statistical T-tests.
Directory of Open Access Journals (Sweden)
Xiaowei Zhang
2012-09-01
Full Text Available Driving patterns exert an important influence on the fuel economy of vehicles, especially hybrid electric vehicles. This paper aims to build a method to identify driving patterns with enough accuracy and less sampling time compared than other driving pattern recognition algorithms. Firstly a driving pattern identifier based on a Learning Vector Quantization neural network is established to analyze six selected representative standard driving cycles. Micro-trip extraction and Principal Component Analysis methods are applied to ensure the magnitude and diversity of the training samples. Then via Matlab/Simulink, sample training simulation is conducted to determine the minimum neuron number of the Learning Vector Quantization neural network and, as a result, to help simplify the identifier model structure and reduce the data convergence time. Simulation results have proved the feasibility of this method, which decreases the sampling window length from about 250–300 s to 120 s with an acceptable accuracy. The driving pattern identifier is further used in an optimized co-simulation together with a parallel hybrid vehicle model and improves the fuel economy by about 8%.
User-interfaces for hybrid systems: Analysis and design through hybrid reachability
Oishi, Meeko Mitsuko Karen
Hybrid systems combine discrete state dynamics, which model mode switching, with continuous state dynamics, which model the physical processes themselves. Applications of hybrid system theory to automated systems have traditionally assumed that the controller itself is an automaton which runs in parallel with the system under control. We model human interaction with hybrid systems, which involves the user; the automation's discrete mode-logic, and the underlying continuous dynamics of the physical system. Often in safety-critical systems, user-interfaces display a reduced set of information about the entire system, however must still provide adequate information and must not confuse the user. We present (1) a method of designing a discrete event system abstraction of the hybrid system, in order to verify or design user-interfaces for hybrid human-automation systems, and (2) the relationship between user-interfaces and discrete observability properties. Using a hybrid computational tool for reachability, we find the largest region in which the system can always remain---this is the safe region of operation. By implementing a controller which arises from this computation, we mathematically guarantee that this safe region is invariant. Assigning discrete states to the computed invariant regions, we create a discrete event system from this hybrid system with safety restrictions. This abstraction can then be used in existing interface verification and design methods. A user-interface, modeled as a discrete system, must, not only be reduced (extraneous information has been eliminated), but also "immediately observable". We derive conditions for immediate observability, in which the current state can be constructed from the current output and last occurring event. Based on finite state machine state-reduction techniques, we synthesize an output for remote user-interfaces which fulfills this property. Aircraft are prime examples of complex, safety-critical systems. In
ON THE STABILIZATION OF THE LINEAR HYBRID SYSTEM STRUCTURE
Directory of Open Access Journals (Sweden)
Kirillov
2014-11-01
Full Text Available The linear control hybrid system, consisting of a fi- nite set of subsystems (modes having different dimensions, is considered. The moments of reset time are determined by some complementary function – evolutionary time. This function satisfies the special complementary ordinary differential equation. The mode stabilization problem is solved for some class of piecewise linear controls. The method of stabilization relies on the set of invariant planes, the existence of which is due to the special form of the hybrid system.
Hybrid architecture active wavefront sensing and control system, and method
Feinberg, Lee D. (Inventor); Dean, Bruce H. (Inventor); Hyde, Tristram T. (Inventor)
2011-01-01
According to various embodiments, provided herein is an optical system and method that can be configured to perform image analysis. The optical system can comprise a telescope assembly and one or more hybrid instruments. The one or more hybrid instruments can be configured to receive image data from the telescope assembly and perform a fine guidance operation and a wavefront sensing operation, simultaneously, on the image data received from the telescope assembly.
EXPONENTIAL ESTIMATES FOR STOCHASTIC DELAY HYBRID SYSTEMS WITH MARKOVIAN SWITCHING
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
This paper deals with the problem of norm bounds for the solutions of stochastic hybrid systems with Markovian switching and time delay. Based on Lyapunov-Krasovskii theory for functional differential equations and the linear matrix inequality (LMI) approach, mean square exponential estimates for the solutions of this class of linear stochastic hybrid systems are derived. Finally, An example is illustrated to show the applicability and effectiveness of our method.
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
Tahmasebi, Pejman; Hezarkhani, Ardeshir
2012-05-01
The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.
Superparamagnetic segmentation by excitable neural systems.
Neirotti, Juan P; Kurcbart, Samuel M; Caticha, Nestor
2003-09-01
Magnetic modeling for clustering or segmentation purposes can either associate the image data to external quenched fields or to the interactions among a set of auxiliary variables. The latter gives rise to superparamagnetic segmentation and is usually done with Potts systems. We have used the superparamagnetic clustering technique to segment images, with the aid of different associated systems. Results using Potts model are comparable to those obtained using excitable FitzHugh-Nagumo and Morris-Lecar model neurons. Interactions between the associated system components are a function of the difference of luminosity on a gray scale of neighbor pixels and the difference of membrane potential.
Application of Hybrid Dynamical Theory to the Cardiovascular System
Laleg-Kirati, Taous-Meriem
2014-10-14
In hybrid dynamical systems, the state evolves in continuous time as well as in discrete modes activated by internal conditions or by external events. In the recent years, hybrid systems modeling has been used to represent the dynamics of biological systems. In such systems, discrete behaviors might originate from unexpected changes in normal performance, e.g., a transition from a healthy to an abnormal condition. Simplifications, model assumptions, and/or modeled (and ignored) nonlinearities can be represented by sudden changes in the state. Modeling cardiovascular system (CVS), one of the most fascinating but most complex human physiological systems, with a hybrid approach, is the focus of this chapter. The hybrid property appears naturally in the CVS thanks to the presence of valves which, depending on their state (closed or open), divide the cardiac cycle into four phases. This chapter shows how hybrid models can be used for modeling the CVS. In addition, it describes a preliminary study on the detection of some cardiac anomalies based on the hybrid model and using the standard observer-based approach.
Note on the Lax Pair of a Coupled Hybrid System
Institute of Scientific and Technical Information of China (English)
LIU Ping; FU Pei-Kai
2012-01-01
The hybrid lattice,known as a discrete Korteweg-de Vries (KdV) equation,is found to be a discrete modified Korteweg-de Vries (mKdV) equation.The coupled hybrid lattice,which is pointed out to be a discrete coupled KdV system,is also found to be a discrete form of a coupled mKdV system.New lax pairs for the single and coupled discrete hybrid systems are proposed as a different study from previous ones.%The hybrid lattice, known as a discrete Korteweg-de Vries (KdV) equation, is found to be a discrete modified Korteweg-de Vries (mKdV) equation. The coupled hybrid lattice, which is pointed out to be a discrete coupled KdV system, is also found to be a discrete form of a coupled mKdV system. New lax pairs for the single and coupled discrete hybrid systems are proposed as a different study from previous ones.
Perception Neural Networks for Active Noise Control Systems
Directory of Open Access Journals (Sweden)
Wang Xiaoli
2012-11-01
Full Text Available In a response to a growing demand for environments of 70dB or less noise levels, many industrial sectors have focused with some form of noise control system. Active noise control (ANC has proven to be the most effective technology. This paper mainly investigates application of neural network on self-adaptation system in active noise control (ANC. An active silencing control system is made which adopts a motional feedback loudspeaker as not a noise controlling source but a detecting sensor. The working fundamentals and the characteristics of the motional feedback loudspeaker are analyzed in detail. By analyzing each acoustical path, identification based adaptive linear neural network is built. This kind of identifying method can be achieved conveniently. The estimated result of each sound channel matches well with its real sound character, respectively.
A HYBRID TECHNIQUE FOR FREQUENCY DOMAIN IDENTIFICATION OF SERVO SYSTEM WITH FRICTION FORCE
Directory of Open Access Journals (Sweden)
SHAIK.RAFI KIRAN,
2011-03-01
Full Text Available The system identification process in servo system with frictional force seems to be a complex task becauseof its non-linear nature. For such non-linear systems, a good choice is system identification in frequencydomain. However, most of the techniques are manual and are inappropriate for determination of systemparameters. This makes system identification ineffective for servo systems with frictional force. Toovercome this issue, a hybrid technique is proposed in this paper. The proposed technique exploits neuralnetwork and genetic algorithm to determine the system parameters of servo systems with friction. In theproposed technique, the target parameters are determined from the transfer function derived for thesystem. Subsequently, the system parameters are identified by a process formed by blending the neuralnetwork and genetic algorithm techniques. Prior to performing the identification procedure, backpropagation training is given to the neural network using a pre-examined dataset. Then with thecombined operation of neural network and genetic algorithm, the system parameters that are closer tothe target parameters for the servo system with frictional force are determined. The technique isimplemented and compared with the existing frequency domain identification technique. From thecomparative results, it is evident that the proposed technique outperforms the existing technique.
Mars Hybrid Propulsion System Trajectory Analysis. Part II; Cargo Missions
Chai, Patrick R.; Merrill, Raymond G.; Qu, Min
2015-01-01
NASA's Human Spaceflight Architecture Team is developing a reusable hybrid transportation architecture in which both chemical and electric propulsion systems are used to send crew and cargo to Mars destinations such as Phobos, Deimos, the surface of Mars, and other orbits around Mars. By combining chemical and electrical propulsion into a single spaceship and applying each where it is more effective, the hybrid architecture enables a series of Mars trajectories that are more fuel-efficient than an all chemical architecture without significant increases in flight times. This paper shows the feasibility of the hybrid transportation architecture to pre-deploy cargo to Mars and Phobos in support of the Evolvable Mars Campaign crew missions. The analysis shows that the hybrid propulsion stage is able to deliver all of the current manifested payload to Phobos and Mars through the first three crew missions. The conjunction class trajectory also allows the hybrid propulsion stage to return to Earth in a timely fashion so it can be reused for additional cargo deployment. The 1,100 days total trip time allows the hybrid propulsion stage to deliver cargo to Mars every other Earth-Mars transit opportunity. For the first two Mars surface mission in the Evolvable Mars Campaign, the short trip time allows the hybrid propulsion stage to be reused for three round-trip journeys to Mars, which matches the hybrid propulsion stage's designed lifetime for three round-trip crew missions to the Martian sphere of influence.
Reachability computation for hybrid systems with Ariadne
L. Benvenuti; D. Bresolin; A. Casagrande; P.J. Collins (Pieter); A. Ferrari; E. Mazzi; T. Villa; A. Sangiovanni-Vincentelli
2008-01-01
htmlabstractAriadne is an in-progress open environment to design algorithms for computing with hybrid automata, that relies on a rigorous computable analysis theory to represent geometric objects, in order to achieve provable approximation bounds along the computations. In this paper we discuss the
Directory of Open Access Journals (Sweden)
Yanqiu Sun
2014-01-01
Full Text Available Stock index forecasting is an important tool for both the investors and the government organizations. However, due to the inherent large volatility, high noise, and nonlinearity of the stock index, stock index forecasting has been a challenging task for a long time. This paper aims to develop a novel hybrid stock index forecasting model named BSO-GNN based on the brain storm optimization (BSO approach and the grey neural network (GNN model by taking full advantage of the grey model in dealing with data with small samples and the neural network in handling nonlinear fitting problems. Moreover, the new developed BSO-GNN, which initializes the parameters in grey neural network with the BSO algorithm, has great capability in overcoming the deficiencies of the traditional GNN model with randomly initialized parameters through solving the local optimum and low forecasting accuracy problems. The performance of the proposed BSO-GNN model is evaluated under the normalization and nonnormalization preprocessing situations. Experimental results from the Shanghai Stock Exchange (SSE Composite Index, the Shenzhen Composite Index, and the HuShen 300 Index opening price forecasting show that the proposed BSO-GNN model is effective and robust in the stock index forecasting and superior to the individual GNN model.
Institute of Scientific and Technical Information of China (English)
刘吉成; 颜苏莉; 乞建勋
2008-01-01
Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, network reliability and the network loss are the main objective of transmission network planning. Combined with set pair analysis (SPA), particle swarm optimization (PSO), neural network (NN), a hybrid particle swarm optimization model was established with neural network and set pair analysis for transmission network planning (HPNS). Firstly, the contact degree of set pair analysis was introduced, the traditional goal set was converted into the collection of the three indicators including the identity degree, difference agree and contrary degree. On this bases, using shi(H), the three objective optimization problem was converted into single objective optimization problem. Secondly, using the fast and efficient search capabilities of PSO, the transmission network planning model based on set pair analysis was optimized. In the process of optimization, by improving the BP neural network constantly training so that the value of the fitness function of PSO becomes smaller in order to obtain the optimization program fitting the three objectives better. Finally, compared HPNS with PSO algorithm and the classic genetic algorithm, HPNS increased about 23% efficiency than THA, raised about 3.7% than PSO and improved about 2.96% than GA.
Kumar, Somesh; Pratap Singh, Manu; Goel, Rajkumar; Lavania, Rajesh
2013-12-01
In this work, the performance of feedforward neural network with a descent gradient of distributed error and the genetic algorithm (GA) is evaluated for the recognition of handwritten 'SWARS' of Hindi curve script. The performance index for the feedforward multilayer neural networks is considered here with distributed instantaneous unknown error i.e. different error for different layers. The objective of the GA is to make the search process more efficient to determine the optimal weight vectors from the population. The GA is applied with the distributed error. The fitness function of the GA is considered as the mean of square distributed error that is different for each layer. Hence the convergence is obtained only when the minimum of different errors is determined. It has been analysed that the proposed method of a descent gradient of distributed error with the GA known as hybrid distributed evolutionary technique for the multilayer feed forward neural performs better in terms of accuracy, epochs and the number of optimal solutions for the given training and test pattern sets of the pattern recognition problem.
A hybrid reconfigurable solar and wind energy system
Gadkari, Sagar A.
We study the feasibility of a novel hybrid solar-wind hybrid system that shares most of its infrastructure and components. During periods of clear sunny days the system will generate electricity from the sun using a parabolic concentrator. The concentrator is formed by individual mirror elements and focuses the light onto high intensity vertical multi-junction (VMJ) cells. During periods of high wind speeds and at night, the same concentrator setup will be reconfigured to channel the wind into a wind turbine which will be used to harness wind energy. In this study we report on the feasibility of this type of solar/wind hybrid energy system. The key mechanisms; optics, cooling mechanism of VMJ cells and air flow through the system were investigated using simulation tools. The results from these simulations, along with a simple economic analysis giving the levelized cost of energy for such a system are presented. An iterative method of design refinement based on the simulation results was used to work towards a prototype design. The levelized cost of the system achieved in the economic analysis shows the system to be a good alternative for a grid isolated site and could be used as a standalone system in regions of lower demand. The new approach to solar wind hybrid system reported herein will pave way for newer generation of hybrid systems that share common infrastructure in addition to the storage and distribution of energy.
Energy Efficient Hybrid Dual Axis Solar Tracking System
Directory of Open Access Journals (Sweden)
Rashid Ahammed Ferdaus
2014-01-01
Full Text Available This paper describes the design and implementation of an energy efficient solar tracking system from a normal mechanical single axis to a hybrid dual axis. For optimizing the solar tracking mechanism electromechanical systems were evolved through implementation of different evolutional algorithms and methodologies. To present the tracker, a hybrid dual-axis solar tracking system is designed, built, and tested based on both the solar map and light sensor based continuous tracking mechanism. These light sensors also compare the darkness and cloudy and sunny conditions assisting daily tracking. The designed tracker can track sun’s apparent position at different months and seasons; thereby the electrical controlling device requires a real time clock device for guiding the tracking system in seeking solar position for the seasonal motion. So the combination of both of these tracking mechanisms made the designed tracker a hybrid one. The power gain and system power consumption are compared with a static and continuous dual axis solar tracking system. It is found that power gain of hybrid dual axis solar tracking system is almost equal to continuous dual axis solar tracking system, whereas the power saved in system operation by the hybrid tracker is 44.44% compared to the continuous tracking system.
López-Caraballo, C H; Salfate, I; Rojas, P; Rivera, M; Palma-Chilla, L
2015-01-01
In this study, an artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey--Glass chaotic time series in the short-term $x(t+6)$. The performance prediction was evaluated and compared with another studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called {\\it stochastic} hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute uncertainties of predictions for noisy Mackey--Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level ($\\sigma_{N}$) from 0.01 to 0.1.
Mayorova, T D; Kosevich, I A
2013-12-01
Serotonin is a widespread neurotransmitter which is present in almost all animal phyla including lower metazoans such as Cnidaria. Serotonin detected in the polyps of several cnidarian species participates in the functioning of a neural system. It was suggested that serotonin coordinates polyp behavior. For example, serotonin may be involved in muscle contraction and/or cnidocyte discharge. However, the role of serotonin in cnidarians is not revealed completely yet. The aim of this study was to investigate the neural system of Cladonema radiatum polyps. We detected the net of serotonin-positive processes within the whole hydranth body using anti-serotonin antibodies. The hypostome and tentacles had denser neural net in comparison with the gastric region. Electron microscopy revealed muscle processes throughout the hydranth body. Neural processes with specific vesicles and neurotubules in their cytoplasm were also shown at an ultrastructural level. This work demonstrates the structure of serotonin-positive neural system and smooth muscle layer in C. radiatum hydranths.
Real-Time Control Strategy of Elman Neural Network for the Parallel Hybrid Electric Vehicle
Directory of Open Access Journals (Sweden)
Ruijun Liu
2014-01-01
Full Text Available Through researching the instantaneous control strategy and Elman neural network, the paper established equivalent fuel consumption functions under the charging and discharging conditions of power batteries, deduced the optimal control objective function of instantaneous equivalent consumption, established the instantaneous optimal control model, and designs the Elman neural network controller. Based on the ADVISOR 2002 platform, the instantaneous optimal control strategy and the Elman neural network control strategy were simulated on a parallel HEV. The simulation results were analyzed in the end. The contribution of the paper is that the trained Elman neural network control strategy can reduce the simulation time by 96% and improve the real-time performance of energy control, which also ensures the good performance of power and fuel economy.
Hierarchical Fault Diagnosis for a Hybrid System Based on a Multidomain Model
Directory of Open Access Journals (Sweden)
Jiming Ma
2015-01-01
Full Text Available The diagnosis procedure is performed by integrating three steps: multidomain modeling, event identification, and failure event classification. Multidomain model can describe the normal and fault behaviors of hybrid systems efficiently and can meet the diagnosis requirements of hybrid systems. Then the multidomain model is used to simulate and obtain responses under different failure events; the responses are further utilized as a priori information when training the event identification library. Finally, a brushless DC motor is selected as the study case. The experimental result indicates that the proposed method could identify the known and unknown failure events of the studied system. In particular, for a system with less response information under a failure event, the accuracy of diagnosis seems to be higher. The presented method integrates the advantages of current quantitative and qualitative diagnostic procedures and can distinguish between failures caused by parametric and abrupt structure faults. Another advantage of our method is that it can remember unknown failure types and automatically extend the adaptive resonance theory neural network library, which is extremely useful for complex hybrid systems.
Directory of Open Access Journals (Sweden)
Yaojie Yue
2016-12-01
Full Text Available Crop frost, one kind of agro-meteorological disaster, often causes significant loss to agriculture. Thus, evaluating the risk of wheat frost aids scientific response to such disasters, which will ultimately promote food security. Therefore, this paper aims to propose an integrated risk assessment model of wheat frost, based on meteorological data and a hybrid fuzzy neural network model, taking China as an example. With the support of a geographic information system (GIS, a comprehensive method was put forward. Firstly, threshold temperatures of wheat frost at three growth stages were proposed, referring to phenology in different wheat growing areas and the meteorological standard of Degree of Crop Frost Damage (QX/T 88-2008. Secondly, a vulnerability curve illustrating the relationship between frost hazard intensity and wheat yield loss was worked out using hybrid fuzzy neural network model. Finally, the wheat frost risk was assessed in China. Results show that our proposed threshold temperatures are more suitable than using 0 °C in revealing the spatial pattern of frost occurrence, and hybrid fuzzy neural network model can further improve the accuracy of the vulnerability curve of wheat subject to frost with limited historical hazard records. Both these advantages ensure the precision of wheat frost risk assessment. In China, frost widely distributes in 85.00% of the total winter wheat planting area, but mainly to the north of 35°N; the southern boundary of wheat frost has moved northward, potentially because of the warming climate. There is a significant trend that suggests high risk areas will enlarge and gradually expand to the south, with the risk levels increasing from a return period of 2 years to 20 years. Among all wheat frost risk levels, the regions with loss rate ranges from 35.00% to 45.00% account for the largest area proportion, ranging from 58.60% to 63.27%. We argue that for wheat and other frost-affected crops, it is
Analysis of the DWPF glass pouring system using neural networks
Energy Technology Data Exchange (ETDEWEB)
Calloway, T.B. Jr.; Jantzen, C.M. [Westinghouse Savannah River Co., Aiken, SC (United States). Savannah River Technology Center; Medich, L.; Spennato, N. [Pavillion Technologies, Inc., Austin, TX (United States)
1997-08-05
Neural networks were used to determine the sensitivity of 39 selected Melter/Melter Off Gas and Melter Feed System process parameters as related to the Defense Waste Processing Facility (DWPF) Melter Pour Spout Pressure during the overall analysis and resolution of the DWPF glass production and pouring issues. Two different commercial neural network software packages were used for this analysis. Models were developed and used to determine the critical parameters which accurately describe the DWPF Pour Spout Pressure. The model created using a low-end software package has a root mean square error of {+-} 0.35 inwc (< 2% of the instrument`s measured range, R{sup 2} = 0.77) with respect to the plant data used to validate and test the model. The model created using a high-end software package has a R{sub 2} = 0.97 with respect to the plant data used to validate and test the model. The models developed for this application identified the key process parameters which contribute to the control of the DWPF Melter Pour Spout pressure during glass pouring operations. The relative contribution and ranking of the selected parameters was determined using the modeling software. Neural network computing software was determined to be a cost-effective software tool for process engineers performing troubleshooting and system performance monitoring activities. In remote high-level waste processing environments, neural network software is especially useful as a replacement for sensors which have failed and are costly to replace. The software can be used to accurately model critical remotely installed plant instrumentation. When the instrumentation fails, the software can be used to provide a soft sensor to replace the actual sensor, thereby decreasing the overall operating cost. Additionally, neural network software tools require very little training and are especially useful in mining or selecting critical variables from the vast amounts of data collected from process computers.
Hybrid Ventilation with Innovative Heat Recovery—A System Analysis
Directory of Open Access Journals (Sweden)
Bengt Hellström
2013-02-01
Full Text Available One of the most important factors when low energy houses are built is to have good heat recovery on the ventilation system. However, standard ventilation units use a considerable amount of electricity. This article discusses the consequences on a system level of using hybrid ventilation with heat recovery. The simulation program TRNSYS was used in order to investigate a ventilation system with heat recovery. The system also includes a ground source storage and waste water heat recovery system. The result of the analysis shows that the annual energy gain from ground source storage is limited. However, this is partly a consequence of the fact that the well functioning hybrid ventilation system leaves little room for improvements. The analysis shows that the hybrid ventilation system has potential to be an attractive solution for low energy buildings with a very low need for electrical energy.
Hybrid rocket propulsion systems for outer planet exploration missions
Jens, Elizabeth T.; Cantwell, Brian J.; Hubbard, G. Scott
2016-11-01
Outer planet exploration missions require significant propulsive capability, particularly to achieve orbit insertion. Missions to explore the moons of outer planets place even more demanding requirements on propulsion systems, since they involve multiple large ΔV maneuvers. Hybrid rockets present a favorable alternative to conventional propulsion systems for many of these missions. They typically enjoy higher specific impulse than solids, can be throttled, stopped/restarted, and have more flexibility in their packaging configuration. Hybrids are more compact and easier to throttle than liquids and have similar performance levels. In order to investigate the suitability of these propulsion systems for exploration missions, this paper presents novel hybrid motor designs for two interplanetary missions. Hybrid propulsion systems for missions to Europa and Uranus are presented and compared to conventional in-space propulsion systems. The hybrid motor design for each of these missions is optimized across a range of parameters, including propellant selection, O/F ratio, nozzle area ratio, and chamber pressure. Details of the design process are described in order to provide guidance for researchers wishing to evaluate hybrid rocket motor designs for other missions and applications.
Energy Technology Data Exchange (ETDEWEB)
Stroh, Christoph; Schnoerch, Stefan; Rathberger, Christian [Magna Powertrain Engineering Center Steyr GmbH und Co. KG, St. Valentin (Austria)
2012-11-01
In the past few years hybrid vehicles have been in the center of automotive engineering efforts, in particular in the field of passenger cars. But hybrid powertrains will also be important for commercial trucks. This focus on hybrid vehicles leads to high demands on thermal management since the additional components in a hybrid vehicle need appropriate cooling or even heating. In the given paper the simulation of a complete cooling system of a hybrid commercial vehicle will be explained. For this virtual examination the commercial 1D thermal management software KULI will be used, a co-simulation with several programs will not be done deliberately. Yet all aspects which are relevant for a global assessment of the thermal management are considered. The main focus is put on the investigation of appropriate concepts for the fluid circuits, including low and high temperature circuits, electric water pumps, etc. Moreover, also a refrigerant circuit with a chiller for active battery cooling will be used, the appropriate control strategy is implemented as well. For simulating transient profiles a simple driving simulation model is included, using road profile, ambient conditions, and various vehicle parameters as input. In addition an engine model is included which enables the investigation of fuel consumption potentials. This simulation model shows how the thermal management of a hybrid vehicle can be investigated with a single program and with reasonable effort. (orig.)
Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling
Directory of Open Access Journals (Sweden)
David Breuer
2014-03-01
Full Text Available How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such nonadditive dendritic processing on single-neuron responses and the performance of associative-memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality.
NNIC—neural network image compressor for satellite positioning system
Danchenko, Pavel; Lifshits, Feodor; Orion, Itzhak; Koren, Sion; Solomon, Alan D.; Mark, Shlomo
2007-04-01
We have developed an algorithm, based on novel techniques of data compression and neural networks for the optimal positioning of a satellite. The algorithm is described in detail, and examples of its application are given. The heart of this algorithm is the program NNIC—neural network image compressor. This program was developed for compression color and grayscale images with artificial neural networks (ANNs). NNIC applies three different methods for compression. Two of them are based on neural networks architectures—multilayer perceptron and kohonen network. The third is based on a widely used method of discrete cosine transform, the basis for the JPEG standard. The program also serves as a tool for determining numerical and visual quality parameters of compression and comparison between different methods. A number of advantages and disadvantages of the compression using ANNs were discovered in the course of the present research, some of them presented in this report. The thrust of the report is the discussion of ANNs implementation problems for modern platforms, such as a satellite positioning system that include intensive image flowing and processing.
Neural-Fuzzy Approach for System Identification.
Tien, B.T.
1997-01-01
Most real-world processes have nonlinear and complex dynamics. Conventional methods of constructing nonlinear models from first principles are time consuming and require a level of knowledge about the internal functioning of the system that is often not available. Consequently, in such cases a nonli
Analysis of advanced solar hybrid desiccant cooling systems for buildings
Energy Technology Data Exchange (ETDEWEB)
Schlepp, D.; Schultz, K.
1984-10-01
This report describes an assessment of the energy savings possible from developing hybrid desiccant/vapor-compression air conditioning systems. Recent advances in dehumidifier design for solar desiccant cooling systems have resulted in a dehumidifier with a low pressure drop and high efficiency in heat and mass transfer. A recent study on hybrid desiccant/vapor compression systems showed a 30%-80% savings in resource energy when compared with the best conventional systems with vapor compression. A system consisting of a dehumidifier with vapor compression subsystems in series was found to be the simplest and best overall performer.
Evolutionary Computation and Its Applications in Neural and Fuzzy Systems
Directory of Open Access Journals (Sweden)
Biaobiao Zhang
2011-01-01
Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.
A PRODUCT HYBRID GMRES ALGORITHM FOR NONSYMMETRIC LINEAR SYSTEMS
Institute of Scientific and Technical Information of China (English)
Bao-jiang Zhong
2005-01-01
It has been observed that the residual polynomials resulted from successive restarting cycles of GMRES(m) may differ from one another meaningfully. In this paper, it is further shown that the polynomials can complement one another harmoniously in reducing the iterative residual. This characterization of GMRES(m) is exploited to formulate an efficient hybrid iterative scheme, which can be widely applied to existing hybrid algorithms for solving large nonsymmetric systems of linear equations. In particular, a variant of the hybrid GMRES algorithm of Nachtigal, Reichel and Trefethen (1992) is presented. It is described how the new algorithm may offer significant performance improvements over the original one.
Directory of Open Access Journals (Sweden)
Zeng Jie
2016-01-01
Full Text Available Wind power forecasting, which is necessary for wind farm, is significant to the dispatch of power grid since the characteristics of wind change intermittently. In this paper, a hybrid model for short-term wind power forecasting based on MIV, Tversky model and GA-BP neural network is formulated. The Mean Impact Value (MIV method is used to monitor the input variable of BP neural network which will simplify the neural network model and reduce the training time. And the Tversky model is used for cluster analysis, which keeps watch over the similar training set of BP neural network. In addition, the Genetic Algorithm (GA is used to optimize the initial weights and thresholds of BP neural network to achieve the global optimization. Simulation results show that the method combined with MIV, Tversky and GA-BP can improve the accuracy of short-term wind power forecasting.
Exploring the function of neural oscillations in early sensory systems
Directory of Open Access Journals (Sweden)
Kilian Koepsell
2010-05-01
Full Text Available Neuronal oscillations appear throughout the nervous system, in structures as diverse as the cerebral cortex, hippocampus, subcortical nuclei and sense organs. Whether or not neural rhythms contribute to normal function, are merely epiphenomena, or even interfere with physiological processing are topics of vigorous debate. Sensory pathways are ideal for investigation of oscillatory activity because their inputs can be defined. Thus, we will focus on sensory systems as we ask how neural oscillations arise and how they might encode information about the stimulus. We will highlight recent work in the early visual pathway that shows how oscillations can multiplex different types of signals to increase the amount of information that spike trains encode and transmit. Last, we will describe oscillation-based models of visual processing and explore how they might guide further research.
Using Neural Networks to improve classical Operating System Fingerprinting techniques
Sarraute, Carlos
2010-01-01
We present remote Operating System detection as an inference problem: given a set of observations (the target host responses to a set of tests), we want to infer the OS type which most probably generated these observations. Classical techniques used to perform this analysis present several limitations. To improve the analysis, we have developed tools using neural networks and Statistics tools. We present two working modules: one which uses DCE-RPC endpoints to distinguish Windows versions, and another which uses Nmap signatures to distinguish different version of Windows, Linux, Solaris, OpenBSD, FreeBSD and NetBSD systems. We explain the details of the topology and inner workings of the neural networks used, and the fine tuning of their parameters. Finally we show positive experimental results.
Spiking Neural P Systems with Neuron Division and Dissolution
Liu, Xiyu; Wang, Wenping
2016-01-01
Spiking neural P systems are a new candidate in spiking neural network models. By using neuron division and budding, such systems can generate/produce exponential working space in linear computational steps, thus provide a way to solve computational hard problems in feasible (linear or polynomial) time with a “time-space trade-off” strategy. In this work, a new mechanism called neuron dissolution is introduced, by which redundant neurons produced during the computation can be removed. As applications, uniform solutions to two NP-hard problems: SAT problem and Subset Sum problem are constructed in linear time, working in a deterministic way. The neuron dissolution strategy is used to eliminate invalid solutions, and all answers to these two problems are encoded as indices of output neurons. Our results improve the one obtained in Science China Information Sciences, 2011, 1596-1607 by Pan et al. PMID:27627104
Hybrid Shipboard Microgrids: System Architectures and Energy Management Aspects
DEFF Research Database (Denmark)
Othman @ Marzuki, Muzaidi Bin; Anvari-Moghaddam, Amjad; Guerrero, Josep M.
2017-01-01
such as renewables (e.g., solar PV, wind power) and conventionals (e.g., diesel engines) as well as energy storage systems (ESSs) such as batteries, fuel cells and flywheels. To optimally manage different energy sources in a shipboard microgrid while meeting different technical/environmental constraints......Strict regulation on emissions of air pollutants imposed by the maritime authorities has led to the introduction of hybrid microgrids to the shipboard power systems (SPSs) which acts toward energy efficient ships with less pollution. A hybrid energy system can include different means of generation......, it is necessary to set up an energy management system. This paper provides an overview of hybrid shipboard microgrids and discusses different methods of power and energy management in such systems which are essential for control, monitoring and optimizing the overall system performance in various mission profiles....
Hybrid Intrusion Detection System for DDoS Attacks
Directory of Open Access Journals (Sweden)
Özge Cepheli
2016-01-01
Full Text Available Distributed denial-of-service (DDoS attacks are one of the major threats and possibly the hardest security problem for today’s Internet. In this paper we propose a hybrid detection system, referred to as hybrid intrusion detection system (H-IDS, for detection of DDoS attacks. Our proposed detection system makes use of both anomaly-based and signature-based detection methods separately but in an integrated fashion and combines the outcomes of both detectors to enhance the overall detection accuracy. We apply two distinct datasets to our proposed system in order to test the detection performance of H-IDS and conclude that the proposed hybrid system gives better results than the systems based on nonhybrid detection.
Energy-Efficient Building HVAC Control Using Hybrid System LBMPC
Aswani, Anil; Taneja, Jay; Krioukov, Andrew; Culler, David; Tomlin, Claire
2012-01-01
Improving the energy-efficiency of heating, ventilation, and air-conditioning (HVAC) systems has the potential to realize large economic and societal benefits. This paper concerns the system identification of a hybrid system model of a building-wide HVAC system and its subsequent control using a hybrid system formulation of learning-based model predictive control (LBMPC). Here, the learning refers to model updates to the hybrid system model that incorporate the heating effects due to occupancy, solar effects, outside air temperature (OAT), and equipment, in addition to integrator dynamics inherently present in low-level control. Though we make significant modeling simplifications, our corresponding controller that uses this model is able to experimentally achieve a large reduction in energy usage without any degradations in occupant comfort. It is in this way that we justify the modeling simplifications that we have made. We conclude by presenting results from experiments on our building HVAC testbed, which s...
Use of hybrid renewable energy systems for small communities
Directory of Open Access Journals (Sweden)
Bandoc Georgeta
2016-01-01
Full Text Available The purpose of this article is to present how the sizing of a hybrid renewable energy system is done for a community of three hundred and five households located in a Delta, starting from the optimization of hybrid energy system for a single household. The methodology used in solving this problem is based on multiple options. The first option consists in determined energy needs, maximum power consumption in cold season and in adapting the solution for the production of electricity by a hybrid plant. The second option consists of energy needs resulted in average consumption of electricity in warm season and in adapting the solution for the production of electricity from a hybrid plant. In conjunction with the demand for electricity for the entire community one will get energy demand by aggregating household level (kWh/household. The novelty of this approach lies in the method used by these hybrid systems for obtaining electricity in small communities, isolated from this case study. Based on the results obtained the method can be expanded the implementation of these projects that use hybrid renewable energy systems.
Split-gene system for hybrid wheat seed production.
Kempe, Katja; Rubtsova, Myroslava; Gils, Mario
2014-06-24
Hybrid wheat plants are superior in yield and growth characteristics compared with their homozygous parents. The commercial production of wheat hybrids is difficult because of the inbreeding nature of wheat and the lack of a practical fertility control that enforces outcrossing. We describe a hybrid wheat system that relies on the expression of a phytotoxic barnase and provides for male sterility. The barnase coding information is divided and distributed at two loci that are located on allelic positions of the host chromosome and are therefore "linked in repulsion." Functional complementation of the loci is achieved through coexpression of the barnase fragments and intein-mediated ligation of the barnase protein fragments. This system allows for growth and maintenance of male-sterile female crossing partners, whereas the hybrids are fertile. The technology does not require fertility restorers and is based solely on the genetic modification of the female crossing partner.
Hybrid Communication System Based on OFDM
Directory of Open Access Journals (Sweden)
2013-11-01
Full Text Available A Hybrid architecture between terrestrial and satellite networks based on Orthogonal Frequency Division Multiplexing (OFDM is employed here. In hybrid architecture, the users will be able to avail the services through the terrestrial networks as well as the satellite networks. The users located in urban areas will be served by the existing terrestrial mobile networks and similarly the one located in rural areas will be provided services through the satellite networks. The technique which is used to achieve this objective is called Pre-FFT adaptive beamforming also called time domain beamforming. When the data is received at the satellite end, the Pre-FFT adaptive beamforming extracts the desired user data from the interferer user by applying the complex weights to the received symbol. The weight for the next symbol is then updated by Least Mean Square (LMS algorithm and then is applied to it. This process is carried out till all the desired user data is extracted.
Neuroeconomics--from neural systems to economic behaviour.
Braeutigam, Sven
2005-11-15
Neuroeconomics is a new and highly interdisciplinary field. Drawing from theories and methodologies employed in both economics and neuroscience, it aims at understanding the neural systems supporting and affecting economically relevant behaviour in real-life situations. Although incomplete, the evidence is beginning to clarify with the possibility that neuroeconomic methodology might eventually trace whole processes of economically relevant behaviour. This paper accompanies the author's ConNEcs 2004 keynote speech on applications of neuroeconomic research.
Adaptive control of system with hysteresis using neural networks
Institute of Scientific and Technical Information of China (English)
Li Chuntao; Tan Yonghong
2006-01-01
An adaptive control scheme is developed for a class of single-input nonlinear systems preceded by unknown hysteresis, which is a non-differentiable and multi-value mapping nonlinearity. The controller based on the three-layer neural network (NN), whose weights are derived from Lyapunov stability analysis, guarantees closed-loop semiglobal stability and convergence of the tracking errors to a small residual set. An example is used to confirm the effectiveness of the proposed control scheme.
Analysis of Hybrid-Electric Propulsion System Designs for Small Unmanned Aircraft Systems
2010-03-01
arrival of the Insight, nearly every major automotive manufacturer has released its own hybrid model. The Toyota Prius , released to the US in 2001, has...dominated the hybrid marketplace with US sales topping 1,000,000 in March 2009.17 The Prius features a power-split hybrid system enabling use of an
Rapid Global Calibration Technology for Hybrid Visual Inspection System.
Liu, Tao; Yin, Shibin; Guo, Yin; Zhu, Jigui
2017-06-19
Vision-based methods for product quality inspection are playing an increasingly important role in modern industries for their good performance and high efficiency. A hybrid visual inspection system, which consists of an industrial robot with a flexible sensor and several stationary sensors, has been widely applied in mass production, especially in automobile manufacturing. In this paper, a rapid global calibration method for the hybrid visual inspection system is proposed. Global calibration of a flexible sensor is performed first based on the robot kinematic. Then, with the aid of the calibrated flexible sensor, stationary sensors are calibrated globally one by one based on homography. Only a standard sphere and an auxiliary target with a 2D planar pattern are applied during the system global calibration, and the calibration process can be easily re-performed during the system's periodical maintenance. An error compensation method is proposed for the hybrid inspection system, and the final accuracy of the hybrid system is evaluated with the deviation and correlation coefficient between the measured results of the hybrid system and Coordinate Measuring Machine (CMM). An accuracy verification experiment shows that deviation of over 95% of featured points are less than ±0.3 mm, and the correlation coefficients of over 85% of points are larger than 0.7.
Towards a General Theory of Stochastic Hybrid Systems
Bujorianu, L.M.; Lygeros, J.; Bujorianu, M. C.
2008-01-01
In this paper we set up a mathematical structure, called Markov string, to obtaining a very general class of models for stochastic hybrid systems. Markov Strings are, in fact, a class of Markov processes, obtained by a mixing mechanism of stochastic processes, introduced by Meyer. We prove that Markov strings are strong Markov processes with the cadlag property. We then show how a very general class of stochastic hybrid processes can be embedded in the framework of Markov strings. This class,...
Neural systems supporting the control of affective and cognitive conflicts.
Ochsner, Kevin N; Hughes, Brent; Robertson, Elaine R; Cooper, Jeffrey C; Gabrieli, John D E
2009-09-01
Although many studies have examined the neural bases of controlling cognitive responses, the neural systems for controlling conflicts between competing affective responses remain unclear. To address the neural correlates of affective conflict and their relationship to cognitive conflict, the present study collected whole-brain fMRI data during two versions of the Eriksen flanker task. For these tasks, participants indicated either the valence (affective task) or the semantic category (cognitive task) of a central target word while ignoring flanking words that mapped onto either the same (congruent) or a different (incongruent) response as the target. Overall, contrasts of incongruent > congruent trials showed that bilateral dorsal ACC, posterior medial frontal cortex, and dorsolateral pFC were active during both kinds of conflict, whereas rostral medial pFC and left ventrolateral pFC were differentially active during affective or cognitive conflict, respectively. Individual difference analyses showed that separate regions of rostral cingulate/ventromedial pFC and left ventrolateral pFC were positively correlated with the magnitude of response time interference. Taken together, the findings that controlling affective and cognitive conflicts depends upon both common and distinct systems have important implications for understanding the organization of control systems in general and their potential dysfunction in clinical disorders.
Hybrid Recommendation System Memanfaatkan Penggalian Frequent Itemset dan Perbandingan Keyword
Suka Parwita, Wayan Gede; Winarko, Edi
2015-01-01
AbstrakRecommendation system sering dibangun dengan memanfaatkan data peringkat item dan data identitas pengguna. Data peringkat item merupakan data yang langka pada sistem yang baru dibangun. Sedangkan, pemberian data identitas pada recommendation system dapat menimbulkan kekhawatiran penyalahgunaan data identitas.Hybrid recommendation system memanfaatkan algoritma penggalian frequent itemset dan perbandingan keyword dapat memberikan daftar rekomendasi tanpa menggunakan data identitas penggu...
Hybrid systems: a real-time interface to control engineering
DEFF Research Database (Denmark)
Eriksen, Thomas Juul; Heilmann, Søren; Holdgaard, Michael
1996-01-01
are usually investigated by control engineers that base their work on the theory of dynamic systems. The mathematical tool for this work is thus mathematical analysis, in particular the theory of differential equations. The paper gives an introduction to a general hybrid systems model for definition of system...
Directory of Open Access Journals (Sweden)
Bahita Mohamed
2011-01-01
Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.
Generalization of Dielectric-Dependent Hybrid Functionals to Finite Systems
Brawand, Nicholas P.; Vörös, Márton; Govoni, Marco; Galli, Giulia
2016-10-01
The accurate prediction of electronic and optical properties of molecules and solids is a persistent challenge for methods based on density functional theory. We propose a generalization of dielectric-dependent hybrid functionals to finite systems where the definition of the mixing fraction of exact and semilocal exchange is physically motivated, nonempirical, and system dependent. The proposed functional yields ionization potentials, and fundamental and optical gaps of many, diverse molecular systems in excellent agreement with experiments, including organic and inorganic molecules and semiconducting nanocrystals. We further demonstrate that this hybrid functional gives the correct alignment between energy levels of the exemplary TTF-TCNQ donor-acceptor system.
Impulsive and hybrid dynamical systems stability, dissipativity, and control
Haddad, Wassim M; Nersesov, Sergey G
2014-01-01
This book develops a general analysis and synthesis framework for impulsive and hybrid dynamical systems. Such a framework is imperative for modern complex engineering systems that involve interacting continuous-time and discrete-time dynamics with multiple modes of operation that place stringent demands on controller design and require implementation of increasing complexity--whether advanced high-performance tactical fighter aircraft and space vehicles, variable-cycle gas turbine engines, or air and ground transportation systems. Impulsive and Hybrid Dynamical Systems goes beyond similar
Energy Technology Data Exchange (ETDEWEB)
Assareh, Ehsanolah; Poultangari, Iman [Dezful Branch, Islamic Azad University, Dezful (Iran, Islamic Republic of); Tandis, Emad [Mechanical Engineering Department, University of Jundi Shapor, Dezful (Iran, Islamic Republic of); Nedael, Mojtaba [Dept. of Energy Engineering, Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran (Iran, Islamic Republic of)
2016-10-15
Enhancing the energy production from wind power in low-wind areas has always been a fundamental subject of research in the field of wind energy industry. In the first phase of this research, an initial investigation was performed to evaluate the potential of wind in south west of Iran. The initial results indicate that the wind potential in the studied location is not sufficient enough and therefore the investigated region is identified as a low wind speed area. In the second part of this study, an advanced optimization model was presented to regulate the torque in the wind generators. For this primary purpose, the torque of wind turbine is adjusted using a Proportional and integral (PI) control system so that at lower speeds of the wind, the power generated by generator is enhanced significantly. The proposed model uses the RBF neural network to adjust the net obtained gains of the PI controller for the purpose of acquiring the utmost electricity which is produced through the generator. Furthermore, in order to edify and instruct the neural network, the optimal data set is obtained by a Hybrid genetic algorithm along with a gravitational search algorithm (HGA-GSA). The proposed method is evaluated by using a 5MW wind turbine manufactured by National Renewable Energy Laboratory (NREL). Final results of this study are indicative of the satisfactory and successful performance of the proposed investigated model.
Fuzzy-Neural Automatic Daylight Control System
Directory of Open Access Journals (Sweden)
Grif H. Şt.
2011-12-01
Full Text Available The paper presents the design and the tuning of a CMAC controller (Cerebellar Model Articulation Controller implemented in an automatic daylight control application. After the tuning process of the controller, the authors studied the behavior of the automatic lighting control system (ALCS in the presence of luminance disturbances. The luminance disturbances were produced by the authors in night conditions and day conditions as well. During the night conditions, the luminance disturbances were produced by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances were produced in two ways: by daylight contributions changes achieved by covering and uncovering a part of the office window and by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances, produced by turning on and off the halogen lamp, have a smaller amplitude than those produced during the night conditions. The luminance disturbance during the night conditions was a helpful tool to select the proper values of the learning rate for CMAC controller. The luminance disturbances during the day conditions were a helpful tool to demonstrate the right setting of the CMAC controller.
Rapid Global Calibration Technology for Hybrid Visual Inspection System
Directory of Open Access Journals (Sweden)
Tao Liu
2017-06-01
Full Text Available Vision-based methods for product quality inspection are playing an increasingly important role in modern industries for their good performance and high efficiency. A hybrid visual inspection system, which consists of an industrial robot with a flexible sensor and several stationary sensors, has been widely applied in mass production, especially in automobile manufacturing. In this paper, a rapid global calibration method for the hybrid visual inspection system is proposed. Global calibration of a flexible sensor is performed first based on the robot kinematic. Then, with the aid of the calibrated flexible sensor, stationary sensors are calibrated globally one by one based on homography. Only a standard sphere and an auxiliary target with a 2D planar pattern are applied during the system global calibration, and the calibration process can be easily re-performed during the system’s periodical maintenance. An error compensation method is proposed for the hybrid inspection system, and the final accuracy of the hybrid system is evaluated with the deviation and correlation coefficient between the measured results of the hybrid system and Coordinate Measuring Machine (CMM. An accuracy verification experiment shows that deviation of over 95% of featured points are less than ±0.3 mm, and the correlation coefficients of over 85% of points are larger than 0.7.
DEFF Research Database (Denmark)
Dou, Chun-Xia; Yue, Dong; Guerrero, Josep M.
2017-01-01
This paper proposes multi-agent system based event- triggered hybrid controls for guaranteeing energy supply of a hybrid energy generation system with high security. First, a mul-ti-agent system is constituted by an upper-level central coordi-nated control agent combined with several lower...... switching control, distributed dynamic regulation and coordinated switching con-trol are designed fully dependent on the hybrid behaviors of all distributed energy resources and the logical relationships be-tween them, and interact with each other by means of the mul-ti-agent system to form hierarchical......-level unit agents. Each lower-level unit agent is responsible for dealing with internal switching control and distributed dynamic regula-tion for its unit system. The upper-level agent implements coor-dinated switching control to guarantee the power supply of over-all system with high security. The internal...
Predictive and Neural Predictive Control of Uncertain Systems
Kelkar, Atul G.
2000-01-01
Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.