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检索条件"主题词=Backpropagation Algorithms"

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Selecting better EEG channels for classification of mental tasks

Selecting better EEG channels for classification of mental t...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： K. Tavakolian A.M. Nasrabadi S. Rezaei Computer Science UNBC Prince George Canada Research Center for Intelligent Signal Processing Tehran Iran

In this work a new method is proposed to reduce the number of EEG channels needed to classify mental tasks. By applying genetic algorithm to the search space consisting of 6 channel combinations of 19 EEG channels the more salient combinations of them in classification of three mental tasks are selected. This algorithm reduces the calculation time and the final results are verified by our observations. Obtained results bring forward the concept of systematic and intelligent selection criteria for choosing superior EEG channels of subjects for mental task classification. This may find applications in the field of brain computer interfaces which are based on classifications of mental tasks, by reducing the number of EEG channels.

关键词： Electroencephalography Genetic algorithms backpropagation algorithms Signal processing algorithms Computer science Application software Brain computer interfaces Data mining Biological neural networks Feature extraction

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A self-adaptive quantum radial basis function network for classification applications

A self-adaptive quantum radial basis function network for cl...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Cheng-Jian Lin Cheng-Hung Chen Chi-Yung Lee Department of Computer Science and Information Engineering Chao Yang University of Technology Taichung Taiwan Department of Electronic Engineering Nankai College Nantou Taiwan

ISBN: (纸本)0780383591

A self-adaptive quantum radial basis function network (QRBFN) is proposed for classification applications. The QRBFN model is a three-layer structure. The hidden layer of the QRBFN model contains quantum function neurons, which are multilevel activation functions. Each quantum function neuron is composed of the sum of sigmoid functions shifted by quantum intervals. A self-adaptive learning algorithm, which consists of the self-clustering algorithm (SCA) and the backpropagation algorithm, is proposed. The proposed the SCA method is a fast, one-pass algorithm for a dynamic estimation of the number of clusters in an input data space. The backpropagation algorithm is used to tune the adjustable parameters. Simulation results were conducted to show the performance and applicability of the proposed model.

关键词： Radial basis function networks Clustering algorithms Neurons Function approximation Partitioning algorithms backpropagation algorithms Neural networks Robust stability Educational institutions Heuristic algorithms

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Predicting the performance of gridFTP transfers

Predicting the performance of gridFTP transfers

引用

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： R.M. Rahman K. Barker R. Alhajj Department of Computer Science University of Calgary Calgary AB Canada

Summary form only given. Replication is a technique in data grid environment that helps to reduce access latency and network bandwidth. Replication also increases data availability and thereby enhances the reliability of the system. Selecting the best replica depends on several factors such as past behavior of the transfer, current state of the network as well as the state of disk device. Here, we develop a predictive framework with a neural network that uses the data from various sources and predicts transfer bandwidth. We compare our results with regression models and demonstrate that the neural network technique outperforms the regression model based predictors for large file transfers.

关键词： Neural networks Throughput Bandwidth Predictive models Delay Availability backpropagation algorithms Programming profession Computer science Drives

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Neuro-sliding mode controller for MIMO systems

Neuro-sliding mode controller for MIMO systems

引用

International Conference on Mechatronics (ICM)

作者： Y. Yildiz A. Sabanovic Faculty of Engineering and Natural Sciences Sabanci University Turkey

In this paper, the previously proposed neuro-sliding mode controller for SISO systems by the authors is modified for MIMO case. The structure benefits from the power of sliding mode control and nonlinear function approximation ability of the neural networks. The controller is a two layer feed-forward neural network and weight updates are done using backpropagation algorithm. The error function that is introduced to the neural network is such that the states of the system are restricted to belong to a certain manifold in state space. Different from the works done until now, in this work the aim is not calculating the equivalent control but instead finding the control input by just minimizing a certain error function. Simulation results demonstrate the performance of the controller.

关键词： Control systems MIMO Sliding mode control Neural networks Function approximation Feedforward systems Feedforward neural networks backpropagation algorithms State-space methods Error correction

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Comparison of two neural network optimization approaches

Comparison of two neural network optimization approaches

引用

International Conference on Mathematical Methods in Electromagnetic Theory (MMET)

作者： E.A. Grimaldi F. Grimaccia M. Mussetta R.E. Zich Dipartimento di Elettrotecnica Politecnico di Milano Milan Italy

This paper compares two optimization methods for training Neural Networks: the typical supervised feed-forward hackpropagation algorithm and an improved Particle Swarm Optimization method. The aim is to highlight advantages and drawbacks of these techniques in order to suitably apply them to electromagnetic problems. Some numerical results and comparisons are presented analyzing a load forecasting problem. Neural Networks are trained for a particular power system load consuption signal, for future time prediction.

关键词： Neural networks Particle swarm optimization Artificial neural networks backpropagation algorithms Optimization methods Space exploration Power system analysis computing Power engineering and energy Management training Electronic mail

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Classification of mental tasks using fixed and adaptive autoregressive models of EEG signals

Classification of mental tasks using fixed and adaptive auto...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Huan Nai-Jen R. Palaniappan Faculty of Information Science and Technology Multimedia University Malacca Malaysia Biomedical Engineering Research Centre Nanyang Technological University Singapore

Classification of EEG signals extracted during mental tasks is a technique for designing brain computer interfaces (BCI). We classify EEG signals that were extracted during mental tasks using fixed autoregressive (FAR) and adaptive AR (AAR) models. Five different mental tasks from 4 subjects were used in the experimental study and combinations of 2 different mental tasks are studied for each subject. Four different feature extraction methods were used to extract features from these EEG signals: FAR coefficients computed with Burg's algorithm using 125 data points, without segmentation and with segmentation of 25 data points, AAR coefficients computed with least-mean-square (LMS) algorithm using 125 data points, without segmentation and with segmentation of 25 data points. Multilayer perception (MLP) neural network (NN) trained by the backpropagation (BP) algorithm is used to classify these features into the different categories representing the mental tasks. The best results for FAR was 92.70% while for AAR was only 81.80%. The results obtained here indicated that FAR using 125 data points without segmentation gave better classification performance as compared to AAR, with all other parameters constant.

关键词： Brain modeling Electroencephalography backpropagation algorithms Feature extraction Multi-layer neural network Neural networks Signal design Brain computer interfaces Data mining Least squares approximation

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Multilayer neural networks in handwritten recognition

Multilayer neural networks in handwritten recognition

引用

International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science

作者： Z. Ciota M. Gawinowski Lods Department of Microelectronics and Computer Science Technical University of Lodz Poland

Multilayer neural network presented in This work and it can be helpful for recognition of handwritten words. Learning efficiency of such network also has been presented. For high learning efficiency a standard backpropagation method has been extended by adding a momentum term. Additional analysis shows how to improve proposed solutions using mixed hardware-software co-design and fuzzy logic approach.

关键词： Multi-layer neural network Neural networks Intelligent networks Handwriting recognition Character recognition Pattern recognition backpropagation algorithms Artificial neural networks Neurons Image recognition

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Simple algorithm for recurrent neural networks that can learn sequence completion

Simple algorithm for recurrent neural networks that can lear...

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International Joint Conference on Neural Networks (IJCNN)

作者： I. Szita A. Lorincz Department of Information Systems Eötvös Lonránd University Budapest Hungary

We can memorize long sequences like melodies or poems and it is intriguing to develop efficient connectionist representations for this problem. Recurrent neural networks have been proved to offer a reasonable approach here. We start from a few axiomatic assumptions and provide a simple mathematical framework that encapsulates the problem. A gradient-descent based algorithm is derived in this framework. Demonstrations on a benchmark problem show the applicability of our approach.

关键词： Recurrent neural networks Signal processing algorithms Neural networks Chaos Humans Prediction methods backpropagation algorithms Information systems Electronic mail Mathematical model

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Modelling of crude oil blending via discrete-time neural networks

Modelling of crude oil blending via discrete-time neural net...

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International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： J. de Jesus Rubio Wen Yu Departmento de Control Automatico CINVESTAV Mexicali Mexico

Crude oil blending is an important unit operation in petroleum refining industry. A good model for the blending system is beneficial for supervision operation, prediction of the export petroleum quality and realizing model-based optimal control. Since the blending cannot follow the ideal mixing rule in practice, we propose a static neural network to approximate the blending properties. By input-to-state stability and dead-zone approaches, we propose a new robust learning algorithm and give theoretical analysis. Real data is applied to illustrate the neuro modeling approache.

关键词： Petroleum Neural networks Stability analysis backpropagation algorithms Refining Mathematical model Automatic control Predictive models Robust stability Uncertainty

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A novel link structure and learning algorithm of feedforward neural network

A novel link structure and learning algorithm of feedforward...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Yan Wu Department of Computer Science and Engineering University of Tongji Shanghai China

The links from hidden layer to output layer are expanded for improving the learning performance of neural network. Based on this, a new neural network structure is proposed and a learning algorithm is derived on it. Several n-parity, function approximation and pattern classification problem simulations are made to verify the effectiveness of the proposed method. The experimental results show that the proposed method has the dual merits of quick training speed and good generalization capability. It proves to be a very effective method.

关键词： Neural networks Feedforward neural networks Neurons Feeds backpropagation algorithms Equations Computer science Electronic mail Joining processes Convergence

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