An annealed chaotic competitive learning network with nonlinear self-feedback and its application in edge detection
An unsupervised parallel approach called Annealed Chaotic Competitive Learning Network ((ACCLN) for the optimization problem is proposed in this paper. The goal is to modify an unsupervised scheme based on the competitive neural network using the chaotic technique governed by an annealing strategy so that on-line learning and parallel implementation to find near-global solution for image edge detection is feasible. In the ACCLN, the edge detection is conceptually considered as a clustering problem. Here, it is a kind of competitive learning network model imposed by a 2-dimensional input layer and an output layer working toward minimizing an objective function defined as the contextual information. The interconnection strength, composed by an internal state and a transient state with a non-linear self-feedback manner, is connected between neurons in input and output layers. To harness the chaotic dynamic and convergence process, an annealing strategy is also embedded into the ACCLN. In addition to retain the characteristics of the conventional neural units, the ACCLN displays a rich range of behavior reminiscent of that observed in neurons. Unlike the conventional neural network, the ACCLN has rich range and flexible dynamics, so that it can be expected to have higher ability of searching for globally optimal or near-optimum results.
annealed chaotic competitive learning network competitive learning network edge detection chaotic dynamic simulated annealing