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检索条件"主题词=Backpropagation Algorithms"

共 1892 条记录，以下是161-170 订阅

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Tuning Neural Networks by Both Connectivity and Size

Tuning Neural Networks by Both Connectivity and Size

引用

International Conference on Information Technology: New Generations (ITNG)

作者： Ian Stewart Wenying Feng Selim Akl School of Computing Queen's University Kingston ONT Canada Computing & Information Systems Trent University Peterborough ONT Canada

This paper presents a new tuning algorithm for a neural network which not only considers the weighting but also the size and connectivity of the network. The approach is done by two parts: the addition of the switch-based hidden nodes and the application of a modified fitness function. The new model is tested by using two simple logic functions. The results show that the modifications lead to the creation of simpler networks, without sacrificing any accuracy or training time in the process. In addition, the lessened human interaction aspect of the new algorithm is also significant in real-time applications.

关键词： Neural networks Genetic algorithms Computer networks Error analysis Network topology Switches Logic functions Humans backpropagation algorithms Information technology

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Study on quality of complex models of dynamic complex systems

Study on quality of complex models of dynamic complex system...

引用

Conference on Human System Interactions, HSI

作者： Grzegorz Dralus Rzeszow University of Technology Rzeszow Poland

ISBN: (纸本)9781424475605;9781424475629

In this paper dynamic global models of input-output complex systems are discussed. In particular, a dynamic complex system which consists of two nonlinear discrete time sub-systems is considered. As a global model multilayer neural networks in a dynamic structure are used. The global model is divided into two sub-models according to the complex system. A quality criterion of global model contains coefficients which define the participation submodels in the global model. Main contribution of this work is the influence study on the global model quality of these coefficients. That influence is examined for a learning algorithm based on gradient descent method for complex neural networks.

关键词： Multi-layer neural network Neural networks Nonlinear dynamical systems Modeling Feedforward neural networks Delay lines Neurofeedback backpropagation algorithms Feedback loop Helium

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The effect of delays on the performance of Layer Recurrent Network

The effect of delays on the performance of Layer Recurrent N...

引用

International Colloquium on Signal Processing & Its Applications, CSPA

作者： Tey Ching Li Farah Hani Nordin Keem Siah Yap Centre for Communications and Service Convergence Technologies Department of Electronics and Communication Engineering College of Engineering Universiti Tenaga Malaysia (UNITEN) Malaysia

ISBN: (纸本)9781424471218;9781424471225

Layer Recurrent Network (LRN) is a dynamic network that has a feedback loop as well as a delay for each layer of the network except for the last layer. The main objective for this research is to study the effect of delays on the performance of the LRN in identifying a nonlinear model. A numerical experiment of the nonlinear model is set up before a set of input and output data is collected. The collected data is then used to train the LRN. The numbers of delays at the feedback loop is manipulated and the effect of the network performance is observed where it shows that the network has the best performance when the number of delay is set to more than the default/original value (which is one).

关键词： Delay effects Neurons Neural networks Feedback loop Transfer functions MATLAB Signal processing algorithms backpropagation algorithms Mathematical model Mutual information

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Character Recognition System Based on Back-Propagation Neural Network

Character Recognition System Based on Back-Propagation Neura...

引用

International Conference on Machine Vision and Human-Machine Interface (MVHI)

作者： Fuliang Li Shuangxi Gao Coll. of Econ. & Manage. Hebei Univ. of Econ. & Bus. Shijiazhuang China Coll. of Inf. & Technol. Hebei Univ. of Econ. & Bus. Shijiazhuang China

According to the characteristics of vehicle license plate, recognition algorithm was proposed based on back-propagation (BP) neural network. Classifier was divided into Chinese characters classifier, English letters classifier, English letters and numbers mixed classifier, and digital classifier these four kinds of classifier in the algorithm. This neural network design can effectively simplify the network structure, improved recognition accuracy and speed. BP algorithm went along improvement as the defects of the standard BP algorithm which had slow convergence and easy to fall into local minimum points. Through simulation experiments, the character recognition system not only has a higher recognition rate, but also has better neural network robustness to decrease failures, that is having good robustness characteristics.

关键词： Character recognition Neural networks Neurons backpropagation algorithms Licenses Vehicles Multi-layer neural network Artificial neural networks Educational institutions Feedforward neural networks

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Using a multilayer perceptrons for accurate modeling of quasi-fractal patch antennas

Using a multilayer perceptrons for accurate modeling of quas...

引用

International Workshop on Antenna Technology: Small Antennas and Novel Metamaterials, iWAT

作者： Paulo H. da F. Silva Elder E. C. Oliveira Adaildo G. d'Assunção Federal Institute of Education Science and Technology of Paraíba João Pessoa PB Brazil Department of Electrical Engineering Federal University of Rio Grande do Norte Natal RN Brazil

ISBN: (纸本)9781424448838;9781424448845

A multilayer perceptrons (MLP) artificial neural network (ANN) with one hidden layer and trained through the efficient resilient backpropagation (RPROP) algorithm is used for modeling quasi-fractal patch antennas. The design of the proposed antenna is based on the application of rectangular Koch fractal curve to the edges of a conventional microstrip inset-fed patch antenna. The electromagnetic (EM) characterization of the patch antennas was performed using the Ansoft DesignerTM software that uses the method of moments. A parametric analysis was developed as function of the dielectric substrate thickness and size of the quasi-fractal patch antennas. Considering the region of interest of the design parameters a representative EM-dataset was obtained to develop the MLP network model using the conventional EM-ANN neuromodeling technique. The MLP model is able to estimates the behavior of the antennas with very good accuracy and low computational cost. Good agreement is observed between simulated and measured results.

关键词： Multilayer perceptrons Patch antennas Artificial neural networks Microstrip antennas backpropagation algorithms Application software Fractals Software design Software performance Moment methods

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Gear Fault Diagnosis with Neural Network Based on Niche Genetic Algorithm

Gear Fault Diagnosis with Neural Network Based on Niche Gene...

引用

International Conference on Machine Vision and Human-Machine Interface (MVHI)

作者： Jia-li Tang Qiu-ru Cai Yi-jun Liu College of Computer Engineering Jiangsu Teachers University of Technology Changzhou China

Because of the complexity of gear working condition, there are non-linear relationship between characteristic parameters and fault types. This paper proposes to apply the artificial neural network theory and the genetic algorithm to solve the difficulties of gear fault diagnosis. Niche technique based on crowding mechanism is used in genetic algorithm, and punishing function is adopted to adjust individual fitness, so as to promote global search capability. Taking a certain gearbox fault signal acquisition experimental system for an example, Matlab software and its neural network toolbox are used to model and simulate. The experiment result shows that the founded network model has good performance for the common gear fault diagnosis and it can identify various types of faults stably and accurately.

关键词： Gears Fault diagnosis Neural networks Genetic algorithms Artificial neural networks Employee welfare Mathematical model backpropagation algorithms Machine vision Man machine systems

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Faster Convergence of BP Network with Hybridization of Improved Cost Function and Control Memory Adaptation

Faster Convergence of BP Network with Hybridization of Impro...

引用

Asia International Conference on Modeling & Simulation

作者： Shafaatunnur Hasan Siti Mariyam Hj. Shamsuddin Soft Computing Research Group K-Economy Research Alliance Universiti Teknologi Malaysia Skudai Malaysia

ISBN: (纸本)9781424471966;9780769540627

Due to the weaknesses of Neural Network (NN) learning, this paper proposes an alternative approach in enhancing NN learning by integrating improved cost function with control adaptation of the nodes and address memory. As commonly known, weight adjustments of NN particularly in Back propagation (BP) algorithm, involve the connections between neurons, the activation function used by the neurons, the learning algorithm that specifies the procedure for adjusting the weights and the cost functions. The cost functions of BP are calculated based on the derivatives. These derivatives will determine the success rate of the application to train the network with an error function that resembles the objective of the problem on hand. Due to that, the concept of weights governance with control part mechanism between the input and hidden layer, and unit offsets of the hidden layer are implemented. to alleviate the problems of BP learning. The address memory part of the network will detain the output pattern of the hidden layer. Subsequently, the output patterns are compared with the input pattern, and propels back to the output layer after learning. From the experiments, we found that the results are promising with these mechanisms and improved cost function which yields faster convergence rates.

关键词： Convergence Cost function Neural networks backpropagation algorithms Neurons Computer networks Weight control Optimization methods Asia Analytical models

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Scattered Points Denoising of TC-Bézier Surface Fitting

Scattered Points Denoising of TC-Bézier Surface Fitting

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International Workshop on Knowledge Discovery and Data Mining (WKDD)

作者： Xumin Liu Weixiang Xu Jing Xu Yong Guan School of Information Engineering Capital Normal University Beijing China School of Traffic and Transportation Beijing Jiaotong University Beijing China

ISBN: (纸本)9781424453979

Fit TC-Bezier surface with radial base function neural network, and build a radial base function network model which is suitable for surface reconstruction. A method is proposed in this paper, which suggests how to denoise and reconstructing free surface with radial base function neural network. This method simulates the internal relation between the points on surface using the study and training of scattered points made by neural cell. The result of simulation experiment shows that this model has strong capability of fitting surface, keeping the topological characteristic of the original points basically, and it also has function of smoothing noise. Besides, the speed of studying is fast and it can get surface of good fairness.

关键词： Surface fitting Scattering Noise reduction Surface reconstruction Neural networks Artificial neural networks Feedforward neural networks Smoothing methods backpropagation algorithms Multi-layer neural network

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Adaptation using neural network in frequency selective MIMO-OFDM systems

Adaptation using neural network in frequency selective MIMO-...

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International Symposium on Wireless Pervasive Computing (ISWPC)

作者： Halil Yigit Adnan Kavak Department of Electronics and Computer Education Kocaeli University Izmit Kocaeli Turkey Department of Computer Engineering Kocaeli University Izmit Kocaeli Turkey

ISBN: (纸本)9781424468553;9781424468584

In this paper, we proposed a neural network (NN) framework as a machine learning technique for link adaptation based on adaptive modulation and coding in 802.11n MIMO-OFDM wireless system to predict the best modulation and coding scheme (MCS) index under packet error rate (PER) constraints. Our approach is compared with the k-nearest neighbour (k-NN) algorithm in frequency selective wireless channels. Simulation results validate the implementation of proposed neural network framework in frequency selective channels, and show that the neural network technique outperforms k-NN algorithm especially in terms of PER when low MCS index selection which provide higher communication reliability is exploited.

关键词： Neural networks Frequency Modulation coding Pervasive computing Machine learning Error analysis Machine learning algorithms backpropagation algorithms Computer networks Receiving antennas

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Hardware implementation of Spiking Neural Network classifiers based on backpropagation-based learning algorithms

Hardware implementation of Spiking Neural Network classifier...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Marco Aurelio Nuno-Maganda Miguel Arias-Estrada Cesar Torres-Huitzil Bernard Girau Computer Science Department National Institute for Astrophysics Optics and Electronics Puebla Mexico Information Technology Department Polytechnic University of Victoria Ciudad Victoria Tamaulipas Mexico CORTEX-TEAM LORIA INRIA Vandoeuvre-les-Nancy France

Spiking Neural Networks (SNNs) have become an important research theme due to new discoveries and advances in neurophysiology, which states that information among neurons is interchanged via pulses or spikes. FPGAs are widely used for implementing high performance digital hardware systems, due to its flexibility and because they are suitable for the implementation of systems with high degree of parallelism. FPGAs have become an important tool because fine grain digital elements useful for efficient hardware implementation of SNNs are provided, making FPGA device suitable for implementing SNNs. Several attempts for implementing efficient classifiers in hardware have been done, but most of them fail because the processing elements are costly in terms of hardware resource utilization. SNNs are less hardware greedy, and the nature of the pulsed processing is well suited to the digital processing blocks of the FPGA devices. In this work, a hardware architecture for implementing both recall and learning phases required for Multilayer FeedForward SNNs is proposed. Results and performance statics are provided.

关键词： Neural network hardware Neural networks backpropagation algorithms Neurons Field programmable gate arrays Artificial neural networks Biological system modeling Logic devices Programmable logic arrays Programmable logic devices

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