Traveling Salesman Problem

The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view. A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered. TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance

Itinerant memory dynamics and global bifurcations in chaotic neural networks

We have considered itinerant memory dynamics in a chaotic neural network composed of four chaotic neurons with synaptic connections determined by two orthogonal stored patterns as a simple example of a chaotic itinerant phenomenon in dynamical associative memory. We have analyzed a mechanism of generating the itinerant memory dynamics with respect to intersection of a pair of ␣ branches of periodic points and collapse of a periodic in-phase attracting set. The intersection of invariant sets is numerically verified by a novel method proposed in this paper. 3,11,12 Among such studies we focus on the associative memory dynamics in this paper. Adachi and Aihara analyzed the dynamics of associative memory networks composed of chaotic neurons in detail and examined characteristics of the retrieval process

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

NEURAL COMPUTATION APPROACH FOR THE MAXIMUM-LIKELIHOOD SEQUENCE ESTIMATION OF COMMUNICATIONS SIGNAL

Abstract: A novel detection approach for signals in digital communications is proposed in this paper by using the neural network with transiently chaos and time-variant gain (NNTCTG) developed by author. The maximum likelihood signal detection problem can be always described as a complex optimization problem with so many local optima that conventional Hopfield-type neural networks cannot be applied. To amend the drawbacks of Hopfield-type networks, the NNTCTG is used to search for globally optimal or near-optimal solutions of the optimization problems with lots of local optima since it has richer and more flexible dynamics over conventional networks only with point attractors. We established a neuro-based detection model for the signal in digital communication and analyzed its working procedure in detail. Two simulation experiments were conducted to illustrate the validity and effectiveness of the proposed approach