Topology-conserving maps for learning visuo-motor-coordination
Neural Networks
New dynamic algorithms for shortest path tree computation
IEEE/ACM Transactions on Networking (TON)
New dynamic SPT algorithm based on a ball-and-string model
IEEE/ACM Transactions on Networking (TON)
Understanding and Restructuring Web Sites with ReWeb
IEEE MultiMedia
The New Shortest Best Path Tree (SBPT) Algorithm for Dynamic Multicast Trees
LCN '99 Proceedings of the 24th Annual IEEE Conference on Local Computer Networks
Destination-driven shortest path tree algorithms
Journal of High Speed Networks
Adding more intelligence to the network routing problem: AntNet and Ga-agents
Applied Soft Computing
Real-time robot path planning based on a modified pulse-coupled neural network model
IEEE Transactions on Neural Networks
A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems
Applied Soft Computing
Dynamic algorithms for the shortest path routing problem: learning automata-based solutions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On Robotic Optimal Path Planning in Polygonal Regions With Pseudo-Euclidean Metrics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Binary Fingerprint Image Thinning Using Template-Based PCNNs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Finding the shortest path in the shortest time using PCNN's
IEEE Transactions on Neural Networks
Object detection using pulse coupled neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Review: Pulse coupled neural networks and its applications
Expert Systems with Applications: An International Journal
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Shortest path tree (SPT) computation is a critical issue in many real world problems, such as routing in networks. It is also a constrained optimization problem, which has been studied by many authors in recent years. Typically, it is solved by heuristic algorithms, such as the famous Dijkstra's algorithm, which can quickly provide a good solution in most instances. However, with the scale of problem increasing, these methods are inefficient and may consume a considerable amount of CPU time. Neural networks, which are massively parallel models, can solve this question easily. This paper presents an efficient modified continued pulse coupled neural network (MCPCNN) model for SPT computation in a large scale instance. The proposed model is topologically organized with only local lateral connections among neurons. The start neuron fires first, and then the firing event spreads out through the lateral connections among the neurons, like the propagation of a wave. Each neuron records its parent, that is, the neighbor which caused it to fire. It proves that the generated wave in the network spreads outward with travel times proportional to the connection weight between neurons. Thus, the generated path is always the global optimal shortest path from the source to all destinations. The proposed model is also applied to generate SPTs for a real given graph step by step. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.