Description: 10。《用于最优化的计算智能》，Nirwan Ansali，Edwin Hou着，李军，边肇棋译 清华大学出版社 1999年第一版
本书从讨论组合优化中的基本问题——NP问题入手，系统地讲述了近年来所发展起来的智能最优化的各种技术和方法，其中包括启发式搜索、Hopfield神经网络、模拟退火和随机机、均场退火以及遗传算法等；并在此基础上，通过一些典型的应用问题，如旅行商问题、模式识别中的点模式匹配问题、通信和任务调度等问题进一步阐明以上一些基本方法怎样用来解决这些原来具有NP性质的困难问题。本书是作者在美国新泽西州理工学院多年讲授有关课程的基础上写成的。全书深入浅出，理论联系实际。为帮助学生掌握基本概念，提高学习能动性，各章编写了习题。本书可作为通信、计算机、控制各专业的高年级学生和研究生学习有关课程的教材。它对于广大科研工作者也是一本很有实际价值的参考书。 -10. "Optimal for Computational Intelligence," Nirwan Ansali, with Edwin Hou, Li Jun, Pin Zhaoxing chess translation Tsinghua University Press in 1999 the first book version of combinatorial optimization from the discussion of basic questions -- NP problem start, the system described in recent years to develop the intelligent optimization of the technology and methods, These include heuristic search, Hopfield networks, simulated annealing and random machine, mean field annealing and genetic algorithms; and on this basis and through some typical applications, such as the traveling salesman problem, pattern recognition of point pattern matching, communication and task scheduling and other issues to further clarify some of these basic methods how to solve these with the original natu Platform: |
Size: 2560416 |
Author:孙东 |
Hits:
Description: 通过模拟退火的HOPFIELD神经网络求解TSP问题-HOPFIELD through simulated annealing neural network for solving the TSP problem Platform: |
Size: 1024 |
Author:yezhenke |
Hits:
Description: 内容包括:人工神经网络简介、单层前向网络及LMS学习算法、多层前向网络及BP学习算法、支持向录机及其学习算法、Hopfield
神经网络,随机神经网络及模拟退火算法、竟争神经网络和协同神纤网络。每章均给出了基于MATLAB的仿真实例以及练习。
-Contents include: Introduction to artificial neural networks, single-layer feedforward network and the LMS learning algorithm, multilayer feedforward network and the BP learning algorithm, support to the recording machine and its learning algorithm, Hopfield neural network, stochastic neural networks and simulated annealing algorithm, actually God of war and synergistic neural network fiber network. Given in each chapter of the simulation based on MATLAB and practice. Platform: |
Size: 6059008 |
Author:小龙 |
Hits:
Description: TSP问题是一个典型的、容易描述但是难以处理的NP完全问题，同时TSP问题也是诸多领域内出现的多种复杂问题的集中概括和简化形式。目前求解TSP问题的主要方法有启发式搜索法、模拟退火算法、遗传算法、Hopfield神经网络算法、二叉树描述算法。所以，有效解决TSP问题在计算理论上和实际应用上都有很高的价值，而且TSP问题由于其典型性已经成为各种启发式的搜索、优化算法的间接比较标准(如遗传算法、神经网络优化、列表寻优(TABU)法、模拟退火法等)。遗传算法就其本质来说，主要是解决复杂问题的一种鲁棒性强的启发式随机搜索算法。因此遗传算法在TSP问题求解方面的应用研究，对于构造合适的遗传算法框架、建立有效的遗传操作以及有效地解决TSP问题等有着多方面的重要意义。-The TSP The problem is a typical, easy to describe but difficult to handle the NP-complete problem, the TSP many areas centralized summarized and simplified form of a variety of complex issues. The main method of solving TSP heuristic search method, simulated annealing, genetic algorithm, Hopfield neural network algorithm, the binary tree to describe the algorithm. Therefore, an effective solution to the TSP has a very high value in the calculation of the theoretical and practical applications, and TSP problem has become due to its typical variety of heuristic search, optimization of indirect comparison standard (such as genetic algorithms, neural networks optimization list optimization (TABU), simulated annealing, etc.). The genetic algorithm is by its very nature, a robustness to solve complex problems heuristic random search algorithm. Genetic algorithm TSP problem solving aspects of applied research, genetic algorithm framework for constructing a suitable, effective genetic manipul Platform: |
Size: 1280000 |
Author:孟晓龙 |
Hits:
Description: SP(旅行商)问题代表组合优化问题,具有很强的工程背景和实际应用价值,但至今尚未找到非常有效的
求解方法.为此,讨论了最近研究比较热门的使用各种智能优化算法(蚁群算法、遗传算法、模拟退火算法、禁忌搜索
算法、Hopfield神经网络、粒子群优化算法、免疫算法等)求解 TSP问题的研究进展,指出了各种方法的优缺点和改
进策略.最后总结并提出了智能优化算法求解 TSP问题的未来研究方向和建议.
-Traveling salesman problem (TSP) is the representation of a kind of combination optimization problems,
possessing a strong engineering background and practical application value. However, there is no effective corre
2 sponding solution to it. A im at that, the research and application of themostpopularmeta
2heuristicmethods such as ant colony algorithm, genetic algorithm, simulated annealing, tabu search, hopfield neural network, particle swarm optimization and immune algorithm, etc. are reviewed. The advantages and disadvantages of each method and the improvement strategies are discussed. The future research direction and sug
Platform: |
Size: 399360 |
Author:chenchen |
Hits:
Search - hopfield annealing