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检索条件"主题词=Backpropagation Algorithms"

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Implementation of an intelligent SINS navigator based on ANFIS

Implementation of an intelligent SINS navigator based on ANF...

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Karim M. Ahjebory Salam A. Ismaeel Ahmed M. Alqaissi Al-ISRA University Amman Jordan Computer Man College for Computer Studies Khartoum Sudan Faculty of Engineering Universiti Putra Malaysia Malaysia

In this work an intelligent navigator developed to overcome the limitations of existing strapdown inertial navigation systems (SINS) algorithm. This system is based on adaptive neuro-fuzzy inference system (ANFIS). As in previous work, which is based on artificial neural network, the window based weight updating strategy was used, and the intelligent navigator evaluated using several SINS hypothetical field tests data. And the results show that the intelligent navigator based on ANFIS more powerful compared with other (traditional and intelligent navigator based on ANN).

关键词： Silicon compounds Artificial neural networks Artificial intelligence Inference algorithms Inertial navigation backpropagation algorithms Vehicles Adaptive systems Fuzzy neural networks Intelligent networks

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Multiple layer perceptron for Direct Inverse Control of a nonlinear system

Multiple layer perceptron for Direct Inverse Control of a no...

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International Conference on Computer, Control and Communication

作者： Vali Uddin Abbas M. Yasir Amir Department of Electronic and Power Engineering National University of Science and Technology Karachi Pakistan

ISBN: (纸本)9781424433131

The idea of using neural networks in control of dynamic system is comparatively new. The well-known and well-established control methodologies such as classical control (PID) and modern control techniques were developed for control of linear systems. However many practical systems are nonlinear. Nonlinear methods for control do exist but vary from case to case. Neural networks offer a simple and extremely flexible solution to problem of control of nonlinear systems. This paper proposes direct inverse control scheme of a simple nonlinear dynamic system. The idea is to train a neural network as an inverse of plant so as to cancel out the plant dynamics and to make plant follow the reference input.

关键词： Nonlinear control systems Control systems Nonlinear systems Neural networks Nonlinear dynamical systems backpropagation algorithms Neurons Power engineering Transfer functions Three-term control

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A type 2 neuron model for classification and regression problems

A type 2 neuron model for classification and regression prob...

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International IEEE/EMBS Conference on Neural Engineering, CNE

作者： Mehmet Onder Efe Department of Electrical and Electronics Engineering TOBB University of Economics and Technology Ankara Turkey

Type 2 fuzzy systems have been under investigation for a while and the projection of type 2 understanding for uncertainty management onto the connectionist models -i.e. neural networks- seems an interesting field of research. This paper considers neurons having multiple bias values defining a new structure that resembles the uncertainty handling capability of type 2 fuzzy models. Such a neuron provides many activation levels that are combined to obtain the neuron response. A neural network with this new model is presented. Several simulation results are shown and the universal approximation property is emphasized.

关键词： Neurons Fuzzy systems Neural networks Power system modeling Uncertainty backpropagation algorithms Neural engineering Conference management Project management Technology management

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Testing artificial metaplasticity in MLP applications

Testing artificial metaplasticity in MLP applications

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Diego Andina Alexis Marcano-Cedeño Joaquín Torres Martin J Alarcón Group for Automation in Signals and Communications Technical University Madrid Madrid Spain

In this work we tested and compared artificial metaplasticity (AMP) results for multilayer perceptrons (MLPs). AMP is a novel artificial neural network (ANN) training algorithm inspired on the biological metaplasticity property of neurons and Shannon's information theory. During training phase, AMP training algorithm gives more relevance to less frequent patterns and subtracts relevance to the frequent ones, claiming to achieve a much more efficient training, while at least maintaining the MLP performance. AMP is specially recommended when few patterns are available to train the network. We implement an artificial metaplasticity MLP (AMMLP) on standard and well-used databases for machine learning. Experimental results show the superiority of AMMLPs when compared with recent results on the same databases.

关键词： Testing Neurons Artificial neural networks Databases backpropagation algorithms Biological system modeling Biological information theory Aerospace industry Cybernetics Automation

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New Fuzzy BP Neural Network Algorithm and Its Application in Stock Market

New Fuzzy BP Neural Network Algorithm and Its Application in...

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International Conference on Computational Intelligence and Software Engineering (CiSE)

作者： Kaiqi Zou Shigang Liu Lidong Xu Information Engineering College Dalian University Dalian China

In this paper new fuzzy neural network BP algorithm is given, and also the feasibility of the way to predict stocks by building knowledge bases under this new fuzzy neural network BP algorithm are discussed. Meanwhile several steps about how to predict the stocks are introduced, the last part illustrates its application in predicting stocks.

关键词： Fuzzy neural networks Neural networks Stock markets Signal processing algorithms Knowledge engineering Educational institutions backpropagation algorithms Mathematical model Clustering algorithms Intelligent networks

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A fuzzy logic based neural network classifier for qualitative classification of odors/gases

A fuzzy logic based neural network classifier for qualitativ...

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International Conference on Emerging Trends in Electronic and Photonic Devices & Systems (ELECTRO)

作者： Ravi Kumar R.R. Das V.N. Mishra R. Dwivedi Dept. of Electronics Eng. Institute of Technology Banaras Hindu University Varanasi India Dept. of Electronics Eng. Institute of Technology Banaras Hindu University Varanasi Varanasi India

ISBN: (纸本)9781424448463

This paper presents a novel approach to odor discrimination using data obtained from the responses of thick film tin oxide sensor array fabricated at our laboratory and employing backpropagation algorithm trained artificial neural network based on fuzzy logic. Fuzzy membership values were used as target vectors to the proposed neural classifier. Three different versions of backpropagation algorithm were used to train the network and their performances have been compared. Superior learning and classification performance was obtained using proposed model trained with TRAINLM version of the backpropagation algorithm.

关键词： Fuzzy logic Neural networks Gases Sensor arrays backpropagation algorithms Artificial neural networks Thick films Tin Thick film sensors Logic arrays

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A Time-Delay Neural Networks Architecture for Structural Damage Detection

A Time-Delay Neural Networks Architecture for Structural Dam...

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International Conference on Advanced Computer Control, ICACC

作者： Lin Niu College of electronic and information engineering University of Chengdu China

In this paper the time-delay neural networks (TDNNs) have been implemented in detecting the damage in bridge structure using vibration signature analysis. A simulation study has been carried out for the incomplete measurement data. It has been observed that TDNNs have performed better than traditional neural networks in this application and the arithmetic of the TDNNs is simple.

关键词： Neural networks Neurons Vibration measurement Bridges backpropagation algorithms Delay effects Spatiotemporal phenomena Computer networks Computer architecture Educational institutions

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Artificial neural networks with ant colony optimization for assessing performance of residential buildings

Artificial neural networks with ant colony optimization for ...

引用

International Seminar on Future BioMedical Information Engineering, FBIE

作者： Huawang Shi Wanqing Li School of Civil Engineering Hebei University of Engineering Handan Hebei China College of Economics and Management Hebei University of Engineering Handan Hebei China

This article established artificial neural networks based on improved ant colony optimization evaluation model for residential performance. Firstly, on the basis of comprehensive analysis of the effects factors of residential building's performance, considering of the advantages of dealing with non-linear object of neural network, the neural network is trained by the sample data. While training neural network, the BP algorithm has good local performance but it is easy to fall into local minimum, and the ant colony algorithm has good global performance, so the following combinatorial method is put forward. Then, the neural network is trained based on ant colony algorithm (ACBP algorithm) in global space, the parameters of neural network is trained using BP algorithm in local space. At last, a case study carried out on the performance assessment of sample residential buildings using the model shows that the ACBP neural network outperforms BP neural network and AC neural network in the aspect of dynamic error forecast is verified by computer emulation example, and related conclusions are given.

关键词： Artificial neural networks Ant colony optimization Neural networks Biological neural networks backpropagation algorithms Biomedical engineering Economic forecasting Yarn Civil engineering Performance analysis

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Comparative Study on River Flow Forecasting Methods of River Networks

Comparative Study on River Flow Forecasting Methods of River...

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WRI World Congress on Software Engineering, WCSE

作者： Rui Wang Jun Xia Wuhan University Wuhan China CAS Institute of Geographic Sciences and Natural Resources Research Beijing China

This paper attempts to set up multivariate linear regression analysis (MLRA) model and 3-layers BP artificial neural network (ANN) mode on river networks and do some comparative researches about them. The applications to the watershed of Tarim indicate that the river flow processes which are simulated separately by two models are satisfactory. They can be the foundation for water resource allocation and scheduling. Above all, through analyzing the structures and forecast precisions of these models, artificial neural network model is better as compared with multivariate linear regression analysis model. In the end, this article puts forward some proposals about how to strengthen the predict abilities of river flow forecasting methods of river networks.

关键词： Rivers Artificial neural networks Linear regression Predictive models Water resources Economic forecasting Resource management backpropagation algorithms Demand forecasting Mathematics

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Learning functions generated by randomly initialized MLPs and SRNs

Learning functions generated by randomly initialized MLPs an...

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IEEE Symposium on Computational Intelligence in Control and Automation, CICA

作者： Ryan Cleaver Ganesh Kumar Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO USA

In this paper, nonlinear functions generated by randomly initialized multilayer perceptrons (MLPs) and simultaneous recurrent neural networks (SRNs) and two benchmark functions are learned by MLPs and SRNs. Training SRNs is a challenging task and a new learning algorithm - PSO-QI is introduced. PSO-QI is a standard particle swarm optimization (PSO) algorithm with the addition of a quantum step utilizing the probability density property of a quantum particle. The results from PSO-QI are compared with the standard backpropagation (BP) and PSO algorithms. It is further verified that functions generated by SRNs are harder to learn than those generated by MLPs but PSO-QI provides learning capabilities of these functions by MLPs and SRNs compared to BP and PSO.

关键词： backpropagation algorithms Neural networks Recurrent neural networks Multilayer perceptrons Multi-layer neural network Particle swarm optimization Testing Space technology Convergence Feedforward neural networks

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