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Analysis of Multi-Layer Neural Network's Recognition Mechanism Using alopex algorithm 10th

World Congress on Medical Physics and Biomedical Engineering

作者： Shintani, Hirohito Akutagawa, Masatake Nagashino, Hirofumi Pandya, Abhijit S. Kinouchi, Yohsuke Univ Tokushima 1-3 Minamijousanjima Tokushima 770 Japan Florida Atlantic Univ Boca Raton FL 33431 USA

ISBN: (纸本)9783540368397

We construct a pattern recognition system by modeling the structure of the visual cortex. The complexities of the visual cortex can be simplified by understanding that the neurons of this region are distinguished by the features of input image that each neuron detects. We propose a neural network(NN) model of the simple structure based on the function and structure of the visual cortex. Moreover, a lot of ideas of manufactured products with NN were proposed. One of issues for productization is uncertainty of the behavior of nonlinearity of NN. Accordingly, it is important to analyze the internal representation of NN. In this paper, we discuss the recognition and training mechanism of a NN model by use of alopex algorithm. alopex algorithm, which is an iterative and stochastic processing to minimize or maximize a cost function. processing to minimize or maximize a cost function. By this method, the receptive fields of the units in the output layer are obtained. We have proposed a four-layered feed-forward NN model for pattern recognition and analyzed the recognition mechanism as well as the performance of the model. We proposed a modified alopex algorithm and calculated the receptive fields of the output unit. In the case of simple training character set, the receptive field changes according to the values of initial weight vectors. If the initial values are large, NN uses small amounts of input values for the classification. In contrast, if the initial values are small, NN uses whole input image. Moreover, it was seen that as the complexity of the set of training patterns increased the receptive field of the output unit changed. Smaller initial values of weight vector have advantage to get the more features. alopex algorithm is an effective method to find the characteristics of images.

关键词： Multi-Layer Neural Networks alopex algorithm visual cortex receptive fields

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A Self-adaptive and Variable Step Length alopex algorithm

A Self-adaptive and Variable Step Length Alopex Algorithm

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International Conference on Materials Science and Information Technology (MSIT 2011)

作者： Li Dong Jiangsu Prov Support Software Engn R&D Ctr Modern Suzhou 215104 Peoples R China

ISBN: (纸本)9783037853191

alopex is a heuristic and optimum algorithm. A self-adaptive and variable step length alopex algorithm was raised to exceed local optimal solution and to approximate global optimal solution based on modified alopex algorithm. To improve modified alopex approximation precision further and eliminate the follow-on shocks appearance, the reasonable alter of delta(in) was implemented. The simulation results show algorithm optimized is practicable and effective.

关键词： alopex algorithm self-adaptive step length

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Optimization of MLP/BP for character recognition using a modified alopex algorithm

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2007年第6期11卷 371-379页

作者： Shintani, Hirohito Akutagawa, Masatake Nagashino, Hirofumi Pandya, Abhijit Kinouchi, Yohsuke Univ Tokushima Fac Engn 2-1mNubanu Hisabhuna Tokushima 7708506 Japan Univ Tokushima Sch Hlth Sci 3-18-15Kuramoto Tokushima 7708509 Japan Florida Atlant Univ Dept Comp Sci & Engn Boca Raton FL 33431 USA

It is difficult to determine the recognition mechanism in the neurons of a neural network trained for pattern recognition due to the non-linear nature of neural networks. This paper describes a recognition mechanism of a four-layer back propagation neural network using alopex algorithm. We have developed a small-scale, four-layered neural network model for simple character recognition, which can recognize the patterns transformed by affined conversion. alopex algorithm is an iterative and stochastic processing method, which was proposed for optimization of a given cost function. In this case the receptive fields of the neurons in the output layers are obtained using the alopex algorithm.

关键词： Neural network alopex algorithm character recognition intelligent systems

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Chemical process dynamic optimization based on hybrid differential evolution algorithm integrated with alopex

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Journal of Central South University 2013年第4期20卷 950-959页

作者：范勤勤吕照民颜学峰郭美锦 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education (East China University of Science and Technology)

To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with alopex and named as alopex-DE, was proposed. In alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of alopex-DE is the best. Further, alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.

关键词： evolutionary computation dynamic optimization differential evolution algorithm alopex algorithm self-adaptivity

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Application of adaptive control to the fluctuation of engine speed at idle

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INFORMATION SCIENCES 2007年第16期177卷 3341-3355页

作者： Kim, DaeEun Park, Jaehong Seoul Natl Univ Sch Elect Engn Seoul 151742 South Korea Univ Leicester Neurobiol Lab Leicester LE1 7RH Leics England

Idle speed control in a fuel-injection engine system has focused on controlling long-term averages of engine speed, but short-term fluctuations of engine speed have been neglected. The torque differences among cylinders influence the idle stability and cause vibration of the vehicle. In this paper, we introduce two intelligent control systems to reduce the fluctuations of engine speed at idle, an evolutionary computing control based on genetic algorithms and a stochastic control based on alopex algorithm. We first estimate the torque differences among the cylinders by observing an engine cycle of crankshaft angular speed. Then the uniformity level over the engine speed is fedback into the control system. It manipulates spark ignition timings to suppress unbalanced combustions among the cylinders. We test the two adaptive approaches with simulation of a nonlinear engine model, and compare their performances. (C) 2007 Elsevier Inc. All rights reserved.

关键词： engine speed fluctuation idle stability spark ignition timing alopex algorithm genetic algorithm

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Stochastic optimization for a tip-tilt adaptive correcting system

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COMPUTER PHYSICS COMMUNICATIONS 2003年第3期150卷 274-292页

作者： Zakynthinaki, MS Saridakis, Y Univ Politecn Cataluna Dept Matemat Aplicada 1 E-08028 Barcelona Spain Tech Univ Crete Dept Sci Appl Math & Comp Lab Khania 73100 Greece

We present computer simulations of a tip-tilt adaptive optics system, where stochastic optimization is applied to the problem of dynamic compensation of atmospheric turbulence. The system uses a simple measure of the light intensity that passes through a mask and is recorded on the image plane, to generate signals for the tip-tilt mirror. A feedback system rotates the mirror adaptively and in phase with the rapidly changing atmospheric conditions. Computer simulations and a series of numerical experiments investigate the implementation of the method in the presence of drifting atmosphere. In particular, the study examines the system's sensitivity to the rate of change of the atmospheric conditions and investigates the optimal size of the mirror's masking area and the algorithm's optimal degree of stochasticity. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： stochastic optimization alopex algorithm adaptive wavefront correction tip-tilt mirror masking

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Stochastic correlative learning algorithms

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2004年第8期52卷 2200-2209页

作者： Haykin, S Chen, Z Becker, S McMaster Univ Commun Res Lab Hamilton ON L8S 4K1 Canada McMaster Univ Dept Psychol Hamilton ON L8S 4K1 Canada

This paper addresses stochastic correlative learning as the basis for a broadly defined class of statistical learning algorithms known collectively as the algorithm of pattern extraction (alopex) family. Starting with the neurobiologically motivated Hebb's rule, the two conventional forms of the alopex algorithm,are derived, followed by a modified variant designed to improve the convergence speed. We next describe two more elaborate versions of the alopex algorithm, which incorporate particle filtering that exemplifies a form of Monte Carlo simulation, to exchange computational complexity for an improved convergence and tracking behavior. In support of the different forms of the alopex algorithm developed herein, we present three different experiments using synthetic and real-life data on binocular fusion of stereo images, on-line prediction, and system identification.

关键词： alopex algorithm binocular fusion financial data prediction particle filtering sequential Monte Carlo estimation Stochastic correlative learning system identification

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Differential evolution algorithm with co-evolution of control parameters and penalty factors for constrained optimization problems

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ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING 2012年第2期7卷 227-235页

作者： Fan, Qinqin Yan, Xuefeng E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

Considering that it is difficult to set suitable penalty factors for the penalty function method, which is one of the most important ways to solve constrained optimization problems, and that the quality of obtained optimal solution mainly depends on the optimization algorithm's performance and handling constraints capacity, a novel differential evolution algorithm with co-evolution of control parameters and penalty factors, named as CoE-DE, is proposed. In CoE-DE, differential evolution operator is applied for evolving the original individuals, which consist of optimal variables. To improve the performance of CoE-DE and the handling constraints capacity, alopex algorithm is used to co-evolve the symbiotic individuals, which consist of two DE control parameters and the penalty factors. To illustrate the whole performance of CoE-DE, several algorithms are applied to solve 13 benchmark functions and five constrained engineering problems. The results show that the performance of CoE-DE is better than SR algorithm and similar to a SIMPILE in 13 benchmark functions, and the satisfactory result is obtained in five constrained engineering problems. Copyright (C) 2010 Curtin University of Technology and John Wiley & Sons, Ltd.

关键词： self-adaptive co-evolution constrained optimization differential evolution algorithm alopex algorithm

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Stochastic optimization for adaptive real-time wavefront correction

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NUMERICAL algorithmS 2003年第1-4期33卷 509-520页

作者： Zakynthinaki, MS Saridakis, YG Tech Univ Crete Appl Math & Comp Lab GR-73100 Khania Crete Greece

We have investigated the performance of an adaptive optics system subjected to changing atmospheric conditions, under the guidance of the alopex stochastic optimization. Atmospheric distortions are smoothed out by means of a deformable mirror, the shape of which can be altered in order to follow the rapidly changing atmospheric phase fluctuations. In a simulation model, the total intensity of the light measured on a central area of the image (masking area) is used as the cost function for our stochastic optimization algorithm, while the surface of the deformable mirror is approximated by a Zernike polynomial expansion. Atmospheric turbulence is simulated by a number of Kolmogorov filters. The method's effectiveness, that is its ability to follow the motion of the turbulent wavefronts, is studied in detail and as it pertains to the size of the mirror's masking area, to the number of Zernike polynomials used and to the degree of the algorithm's stochasticity in relation to the mean rate of change of atmospheric distortions. Computer simulations and a series of numerical experiments are reported to show the successful implementation of the method.

关键词： stochastic optimization alopex algorithm adaptive wavefront correction Zernike polynomials deformable mirror masking

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Sub-region alopex Optimization Method with RSM for Design of Permanent Magnet Machines

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CES Transactions on Electrical Machines and Systems 2018年第2期2卷 207-210页

作者： Xiaoyu Liu Weinong Fu Electrical Engineering Department The Hong Kong Polytechnic UniversityHong Kong

Based on an alopex optimization algorithm and a response surface model(RSM),a hybrid sub-region methodology is presented to solve the optimal design problems of permanent magnet(PM)*** alopex optimization method is processed both in subspace and in global solution *** order to decrease the computing time,a multi quadric radial basis function(MQRBF)is embedded in the *** proposed method speeds up the convergence rate while keeps the accuracy of the solution.A numerical experiment is given to validate the efficiency and effectiveness of the method.

关键词： alopex algorithm finite element method multi quadric radial basis function optimization method radial basis function.

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