Capacity analysis of the asymptotically stable multi-valued exponential bidirectional associative memory

New delay-dependent uniform stability criteria for fractional-order BAM neural networks with discrete and distributed delays

Initially, a class of Caputo fractional-order bidirectional associative memory neural networks in two variables is developed, building upon the groundwork laid by delayed Caputo fractional system in one variable. Next, the Razumikhin-type uniform stability conditions, originally formulated for single-variable systems, are successfully extended to accommodate the complexities of delayed Caputo fractional systems in two variables. Leveraging this extension and employing a suitable Lyapunov function, the delay-dependent uniform stability criteria for the addressed fractional-order bidirectional associative memory neural networks are expressed in terms of linear matrix inequalities. Finally, the effectiveness and practicality of the theoretical findings are demonstrated through the application of two numerical examples, affirming the viability of the proposed approach.

Data compression by the recursive algorithm of exponential bidirectional associative memory

A novel data compression algorithm utilizing the histogram and the high-capacity exponential bidirectional associative memory (eBAM) is presented. Since eBAM has been proved to possess high capacity and fault tolerance, it is suitable to be utilized in the data compression using the table-lookup scheme. The histogram approach is employed to extract the feature vectors in the given data. The result of the simulation of the proposed algorithm turns out to be better than the traditional methods.

Improving the capacity of complex-valued neural networks with a modified gradient descent learning rule

Jankowski et al. proposed (1996) a complex-valued neural network (CVNN) which is capable of storing and recalling gray-scale images. The convergence property of the CVNN has also been proven by means of the energy function approach. However, the memory capacity of the CVNN is very low because they use a generalized Hebb rule to construct the connection matrix. In this letter, a modified gradient descent learning rule (MGDR) is proposed to enhance the capacity of the CVNN. The proposed technique is derived by applying gradient search over a complex error surface. Simulation shows that the capacity of CVNN with MGDR is greatly improved.

A study of pattern recovery in recurrent correlation associative memories

In this paper, we analyze the recurrent correlation associative memory (RCAM) model of Chiueh and Goodman (1990, 1991). This is an associative memory in which stored binary memory patterns are recalled via an iterative update rule. The update of the individual pattern-bits is controlled by an excitation function, which takes as its argument the inner product between the stored memory patterns and the input patterns. Our contribution is to analyze the dynamics of pattern recall when the input patterns are corrupted by noise of a relatively unrestricted class. We show how to identify the excitation function which maximizes the separation (the Fisher discriminant) between the uncorrupted realization of the noisy input pattern and the remaining patterns residing in the memory. The excitation function which gives maximum separation is exponential when the input bit-errors follow a binomial distribution. We develop an expression for the expectation value of bit-error probability on the input pattern after one iteration. We show how to identify the excitation function which minimizes the bit-error probability. The relationship between the excitation functions which result from the two different approaches is examined for a binomial distribution of bit-errors. We develop a semiempirical approach to the modeling of the dynamics of the RCAM.