Estimating the Lyapunov exponents of discrete systems

In the present paper, our aim is to determine both upper and lower bounds for all the Lyapunov exponents of a given finite-dimensional discrete map. To show the efficiency of the proposed estimation method, two examples are given, including the well-known Henon map and a coupled map lattice.

CNN universal machine as classificaton platform: an art-like clustering algorithm

Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be very efficient as a feature detector. The next step is to post-process the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can be mapped to the CNN-UM. Moreover, this mapping is general enough to include different types of feed-forward neural networks. The designed analogic CNN algorithm is capable of classifying the extracted feature vectors keeping the advantages of the ART networks, such as robust, plastic and fault-tolerant behaviors. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. The algorithm is extended for supervised classification. The presented binary feature vector classification is implemented on the existing standard CNN-UM chips for fast classification. The experimental evaluation shows promising performance after 100% accuracy on the training set.

A local and neurobiologically plausible method of learning correlated patterns

The problem of learning correlated patterns in a neurobiologically plausible way without multiple iterations is discussed. Three guiding principles are outlined for solving this problem in a manner suggestive of how the memory modules of the human brain do it. The first, already known, principle of minimum disturbance is applied quantitatively to minimise the number of learning iterations. The second guiding principle involves a self-induced remedy to undo any damage caused to the stabilities of the old stored patterns after a new one has been learnt. The third involves localising connectivities between neurons depending on the structure of the input patterns. A neurobiologically plausible network and a learning rule are constructed based on these principles. Satisfactory use of this network in learning English words as sequences of their letters is demonstrated by means of computer simulation.