Description: 在一个随机的迷宫里，小白鼠如何能最快地从起点走到终点。可采用科学的神经网络或遗传算法来实现，也可以采用基本的办法：每条路都试探。-At a random maze, the mouse, how can the fastest from the starting point of an end. Neural network can be scientific or genetic algorithm to achieve, but also the basic approach can be used: each road test. Platform: |
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Author:冷秋 |
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Description: 本文根据脉冲耦合神经网络（PCNN）并行运行的特点，提出了基于PCNN 模型的迷宫最短路径搜索算法。
从理论上对该算法进行了分析和讨论，并给出了具体的算法和实验结果，验证了该方法的有效性。与其他算法相比，
该方法可以在最短的时间内完成最短路径的搜索。-Based on Pulse Coupled Neural Network (PCNN) run in parallel the characteristics of PCNN model is proposed based on the shortest path maze searching algorithm. In theory the algorithm is analyzed and discussed, and given a specific algorithm and experimental results verify the effectiveness of the method. Compared with other algorithms, the method can be completed within the shortest possible time the shortest path search. Platform: |
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Author:wangxx |
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Description: 本文在脉冲耦合神经网络(PCNN2Pulse Coupled Neural Network) 的基础上,提出了时延脉冲耦合神经网络
(DPCNN2Delay PCNN) ,并将其成功地用于求解最短路径,同时给出了基于DPCNN 的最短路径求解算法. Caulfield 与
Kinser 提出了用PCNN 求解迷宫问题的方法,虽然他们的方法也可用于求解最短路径,但所需神经元的数量巨大,而本
文的方法所需的神经元的数量远小于他们的方法. 同时,本文的方法充分利用了DPCNN 脉冲快速并行传播的特点,可
迅速地求出最短路径,其所需的计算量仅正比于最短路径的长度,与路径图的复杂程度及路径图中的通路总数无关.
计算机仿真结果表明,采用本文的方法,用少量的神经元就可迅速地求出最短路径.- This paper presents DPCNN(Delay Pulse Coupled Neural Network) based on PCNN and uses DPCNN to find the
shortest path successfully.Meanwhile ,the algorithmof finding the shortest path based on DPCNN is described. Caulfield and Kinser in2
troduced the PCNN method to solve the maze problem and although their method also can be used to find the shortest path ,a large
quantity of neurons are needed. However ,the approach proposed in this paper needed very fewer neurons than proposed by Caulfield
and Kinser. In the meantime ,due to the pulse parallel transmission characteristic of DPCNN ,the approach proposed can find the short2
est path quickly. The computational complexity of our approach is only related to the length of the shortest path , and independent to
the path graph complexity and the number of existing paths in the graph. The results of computer simulations show that by using the
approach proposed in this paper ,we can use a small quantity of neurons to find the shortest p Platform: |
Size: 102400 |
Author:wangxx |
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Search - maze neural network