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New learning algorithm of bp network based on particle swarm optimization

Jisuanji Gongcheng/Computer Engineering 2006年第14期32卷 181-183页

作者： Song, Nai-Hua Xing, Qing-Hua Institute of Missile Air-force University of Engineering Sanyuan 713800 China

Standard bp algorithm is a kind of learning algorithm of multilayer perceptrons. It is designed based of gradient method. To overcome its defects, the paper proposes a new learning algorithm of bp network particle swarm learning algorithm. The algorithm adopts parallel technology to quicken the speed, and it is simply achieved by programming. Simulation results indicate that the particle swarm learning algorithm is a simple and efficient learning algorithm, and it has widely application prospect.

关键词： bp algorithm Multilayer perceptrons(MLP) Particle swarm learning algorithm(PSLA) Particle swarm optimization

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MOGA algorithm for multi-objective optimization of aircraft detection

MOGA algorithm for multi-objective optimization of aircraft ...

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20th Congress of the International-Commission-for-Optics

作者： Sun, HG Pan, YX Zhang, JB NE Normal Univ Coll Comp Changchun 130024 Peoples R China ChangChun Univ Sci & Technol Changchun 130022 Peoples R China

ISBN: (纸本)0819460583

This paper presents effective multi-objective genetic algorithms (MOGA) method, whose character lies in that evolutionary population is preference ranked based on concordance model, which was applied to a multi-objective optimization of aircraft, measure of fitness degree was discussed as an emphasis. The solutions were analyzed and compares with original bp neural networks algorithm, which is better than the network trained only on alternating momentum, which can performed well neural networks and have shown the superiority to the network structure. Based the pareto optimal approaches are equipped with a fast identifying ability in capturing the learned objects, and in the meantime it can adapt the new objects. The experiments with variety of image show that the method proposed is efficient and useful, the result demonstrates that convergence speed is faster than traditional algorithm;target was recognized by this algorithm and can increase recognition precision.

关键词： multi-objective genetic algorithms objective recognition Pareto Optimal bp algorithm

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Research on enterprise tacit knowledge management performance appraisal based on artificial neural networks

Research on enterprise tacit knowledge management performanc...

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13th International Conference on Management Science and Engineering

作者： Zhang Qing-pu Shan Wei Harbin Inst Technol Sch Management Harbin 150001 Peoples R China

ISBN: (纸本)7560323553

Based on knowledge management theory and performance appraisal methodology, the enterprise tacit knowledge management performance appraisal index system is established, and in view of the neural network structure characteristic, self-adapted and self-taught function, enterprise tacit knowledge management performance appraisal model based on bp neural algorithm is proposed. This model is feasible and suitable in terms of convergence rate, the network adaptability aspect. By using these research results, it can appraise the level of the enterprise tacit knowledge management scientifically, and provide the policy-making basis to correctly instruct enterprise knowledge management development.

关键词： bp algorithm knowledge management performance appraisal tacit knowledge

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Research on universal fuzzy logic controller

Research on universal fuzzy logic controller

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IEEE International Conference on Mechatronics and Automation

作者： Lu, Bin N China Elect Power Univ Dept Comp Sci & Technol Baoding Hebei Province Peoples R China

ISBN: (纸本)1424404657

Based on the general structure of fuzzy logic controllers, we soften the operations Of fuzzy inferring with a class of universal logic operators, and then present a new type of flexible structured fuzzy logic controller, universal fuzzy logic controller. It can be trained by bp algorithm with adaptive learning rate. That will bring the appropriate control structures according to the different control objects. The simulation result shows that it's effective.

关键词： fuzzy controller universal logics fuzzy neural networks bp algorithm

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A learning algorithm of artificial neural network based on GA - PSO

A learning algorithm of artificial neural network based on G...

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6th World Congress on Intelligent Control and Automation

作者： Du, Shiqiang Li, Wanshe Cao, Kai Shaanxi Normal Univ Coll Math & Informat Sci Xian 710062 Peoples R China

ISBN: (纸本)1424403316

In order to get over the insufficiency of back-propagation (bp) algorithm, after analyses of genetic algorithm (GA) and particle swarm optimization (PSO), a GA-PSO algorithm is proposed. In GA-PSO, individuals in a new generation are created, not only by crossover and mutation operation in GA but also by PSO, based on redefine local optimization swarm. So it can both avoid local minimum and has good global search capacity. The performance of GA-PSO is compared to both GA and PSO in artificial neural networks weight training, demonstrating its superiority.

关键词： bp algorithm genetic algorithm particle swarm optimization GA-PSO algorithm

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Study of fuzzy controller based on neural-network for PMSM speed adjustment system

Study of fuzzy controller based on neural-network for PMSM s...

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6th World Congress on Intelligent Control and Automation

作者： Wang, Limei Tian, Mingxiu Shenyang Univ Technol Shenyang 110023 Peoples R China

ISBN: (纸本)1424403316

A fuzzy intelligent controller based on bp neural network is proposed in this paper for permanent magnet synchronous motor (PMSM) speed control. The intelligent controller can remember fuzzy rulers by neural network, which has not only the simplicity and the nonlinear control ability of fuzzy control, but also the learning and adaptive functions by using neural network. In the double loop of PMSM speed adjustment system, the hysteresis current regulator is implemented in the current loop and the fuzzy intelligent control scheme is applied in the speed loop. The effectiveness of the proposed controller is verified by simulation. Simulation results show that the proposed controller is superior to the traditional PI controller. It has good dynamic and static characteristics because of its advantage in quick response and good robustness.

关键词： PMSM neural network fuzzy control bp algorithm robustness

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Application of artificial neural networks coupled with an orthogonal design to simulation for powder mixed EDM

Application of artificial neural networks coupled with an or...

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Systems and Control in Aerospace and Astronautics, 2006. ISSCAA 2006. 1st International Symposium on

作者： Fuling Zhao Yuangang Wang Hui Wang Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)0780393953

Electric discharge machining (EDM) is one of the most accurate manufacturing processes available, and it is quite indispensable to aerospace industries. Powder mixed EDM (PMEDM) is a new process through which a better surface can be reached, nevertheless, PMEDM is a complicated and random process influenced by many parameters, and the intricacies of mechanism hinders its application. This paper presents a method that can be used to forecast the surface roughness in the PMEDM process with the application of artificial neural networks (ANNs). ANNs with back propagation (bp) algorithm coupled with an orthogonal design was applied to the modeling of the PMEDM system and simulation of experimental conditions. An orthogonal design was utilized to design the experimental protocol, in which peak current, pulse-on time, discharging area, servo voltage and no-load voltage were varied simultaneously. The results showed that the ANNs model reflected the complex relationship of PMEDM and had high predicting accuracy.

关键词： backpropagation electrical discharge machining neural nets production engineering computing surface roughness ANN bp algorithm PMEDM process artificial neural networks back propagation algorithm electric discharge machining orthogonal design powder mixed

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Quality Forecast on Finishing Machining by Free Grinding Tools Based on Genetic Neural Network

Quality Forecast on Finishing Machining by Free Grinding Too...

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The International Conference on Surface Finishing Technology and Surface Engineering

作者： C.H. Song, J.M. Wang and S.C. Yang College of Computer Software Engineering, Taiyuan University of Technology, 030024, China. College of Mechanical and Electrical Engineering, Taiyuan University of Science and Technology, 030024 , China. College of Mechanical Engineering, Taiyuan University of Technology, TaiYuan, 030024, China.

According to the current situation of finish machining by free grinding tools, GA-bp algorithm, which integrates neural network and genetic algorithm together, is applied to finish machining. A model based on genetic neural network has been built to forecast surface quality of machined parts and to choose optimized processing parameters. Finish machining by fluid magnetic grinding tools has been illustrated by comparing theoretical calculations with measured values. The result that the error is only about 5% shows that the algorithm is better. After simple modification, such algorithm could be successfully applied in other finish machining forms by free grinding tools, consequently, reasonable machining methods could be chosen.

关键词： Neural network Genetic algorithm bp algorithm Finish machining technology GA-NN method

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Segmentation method of ultrasonic CT image based on bp neural network

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Wuhan Ligong Daxue Xuebao/Journal of Wuhan University of Technology 2006年第1期28卷 93-95页

作者： Li, Zhuo-Qiu Fang, Xi Song, Xian-Hui School of Sciences Wuhan University of Technology Wuhan 430070 China

In this paper, the bp neural network method for segmenting the target and detecting the edge from the color image was studied. The samples and their corresponding binary images for bp neural network training were derived from the ultrasonic CT images in concrete's non-destructive detecting. The experimental results showed that the segmentation and the edge detecting effects of the bp network method were satisfying. Easy to design was the best advantage of such a method. However the drawback was that bp neural network need too much time for learning.

关键词： Artificial neural network bp algorithm Color image segmentation Concrete

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Intrusion detection method based on improved bp neural network algorithm

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Jisuanji Gongcheng/Computer Engineering 2005年第13期31卷 154-155+158页

作者： Wei, Shengjun Hu, Changzhen Jiang, Fei Network Security Technique Laboratory Beijing Institute of Technology Beijing 100081 China

The bp neural network algorithm is improved to detect intrusion. The improved method is as follows: according to different input, different part of network will be fired to generate output. The weights of the arcs only connected with fired neuron will be adjusted whereas all weights in traditional bp network must be adjusted. The experiment results indicate that this improved algorithm can increase learning speed and shorten training time. The results also show that the intrusion detection rate is improved.

关键词： bp algorithm Function distribution Intrusion detection Neural network

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