神經(jīng)網(wǎng)絡(luò)動(dòng)態(tài)分析和優(yōu)化（英文版）

定　價(jià)：￥40.00

作　者：	楊剛
出版社：	中國(guó)環(huán)境科學(xué)出版社
叢編項(xiàng)：
標(biāo)　簽：	暫缺

購(gòu)買(mǎi)這本書(shū)可以去

京東 (￥40.00)

ISBN：	9787511117687	出版時(shí)間：	2014-07-01	包裝：
開(kāi)本：	16開(kāi)	頁(yè)數(shù)：	124	字?jǐn)?shù)：

內(nèi)容簡(jiǎn)介

　　《神經(jīng)網(wǎng)絡(luò)動(dòng)態(tài)分析和優(yōu)化（英文版）》以霍普菲爾德網(wǎng)絡(luò)和彈性網(wǎng)絡(luò)為重點(diǎn)，集中介紹了神經(jīng)網(wǎng)絡(luò)算法的動(dòng)態(tài)特性和控制方法，顯示了神經(jīng)網(wǎng)絡(luò)復(fù)雜動(dòng)態(tài)對(duì)網(wǎng)絡(luò)性能的重要影響；并在神經(jīng)網(wǎng)絡(luò)動(dòng)態(tài)分析的基礎(chǔ)上，給出了優(yōu)化神經(jīng)網(wǎng)絡(luò)的有效方法和核心理論?！渡窠?jīng)網(wǎng)絡(luò)動(dòng)態(tài)分析和優(yōu)化（英文版）》的諸多方法和理論，展現(xiàn)了神經(jīng)網(wǎng)絡(luò)算法在組合優(yōu)化問(wèn)題上的優(yōu)勢(shì)，為神經(jīng)網(wǎng)絡(luò)算法的優(yōu)化和應(yīng)用，提供了啟發(fā)和指導(dǎo)思想；并且，《神經(jīng)網(wǎng)絡(luò)動(dòng)態(tài)分析和優(yōu)化（英文版）》進(jìn)一步完善了神經(jīng)網(wǎng)絡(luò)基礎(chǔ)理論，具有重要的理論和應(yīng)用價(jià)值。

作者簡(jiǎn)介

暫缺《神經(jīng)網(wǎng)絡(luò)動(dòng)態(tài)分析和優(yōu)化（英文版）》作者簡(jiǎn)介

圖書(shū)目錄

1 BACKGROUND
1.1 Combinatorial optimization and NP-Hard problem
1.2 Traditional methods for combinatorial optimization
1.2.1 Local search
1.2.2 Genetic algorithm
1.2.3 Tabu search
1.2.4 Simulated annealing
1.3 Themes and contributions of the book
2 ARTIFICIAL NEURAL NETWORK
2.1 Biological neural network to artificial neural network
2.2 History of artificial neural network
2.3 Methods of neural networks
2.3.1 Hopfield neural network
2.3.2 Self-organizing map
2.3.3 Maximum neural network
2.3.4 Elastic net
2.4 Problems of neural network
3 IMPROVED HOPFIELD-TYPE NN WITH CHAOTIC DYNAMICS FOR MCP
3.1 Maximum clique problem
3.2 The analysis of HNN with chaotic dynamics
3.2.1 Chaotic neural networks
3.2.2 Transiently chaotic neural network
3.2.3 Relationship between TCNN and CNN
3.2.4 Analysis of parameter selection
3.3 A flexible TCNN for MCP
3.3.1 The flexible annealing strategy
3.3.2 Flexible TCNN and its dynamic analysis
3.3.3 Simulations
3.4 TCNN with filter method for MCP
3.4.1 Analysis on feasibility and adaptivity of TCNN
3.4.2 Algorithm description
3.4.3 Simulations
3.5 Delayed TCNN and its application on MCP
3.5.1 The flaw of variable delayed model
3.5.2 The delayed transiently chaotic neural network
3.5.3 Simulation
3.6 Summary
4 IMPROVED CHAOTIC MNN FOR COPS
4.1 Chaotic maximum neural network
4.2 Improved CMNN with stochastic dynamics for N-Queens
4.2.1 N-Queens problems
4.2.2 Dynamics analysis and Improvement of the CMNN with
stochastic dynamics for N-Queens problems
4.2.3 Algorithm description
4.2.4 Simulations
4.3 Chaotic MNN with flexible annealing strategy for MCP
4.3.1 Improved algorithm and its dynamics analysis
4.3.2 Simulations
4.4 Summary
5 IMPROVED ELASTIC NET FOR TRAVELING SAI-ESMAN PROBLEM
5.1 Elastic net for TSP
5.2 Efficiency analysis on elastic net
5.3 The improved algorithms based on Elastic Net
5.3.1 Rebuilt clone elastic net algorithm
5.3.2 The unsupervised up-to-bottom hierarchical clustering elas.
tic net algorithm
5.3.3 Simulation
5.4 Summary
6 SUMMARY AND FUTURE WORK
6.1 Summary of this book
6.2 Future work
BIBLIOGRAPHY