Associative neural networks are regaining the popularity due to their recent successful application to the problem of real-time memorization and recognition in video. This paper presents a comparative overview of several most popular models of these networks, such as those learnt by the Projective Learning rules and having Sparse architecture, and introduces an Open Source Associative Neural Ne...

The relation existing between support vector machines (SVMs) and recurrent associative memories is investigated. The design of associative memories based on the generalized brain-state-in-a-box (GBSB) neural model is formulated as a set of independent classification tasks, which can be efficiently solved by standard software packages for SVM learning. Some properties of the networks designed in...

a sigmoid function is necessary for creation a chaotic neural network (cnn). in this paper, a new function for cnn is proposed that it can increase the speed of convergence. in the proposed method, we use a novel signal for controlling chaos. both the theory analysis and computer simulation results show that the performance of cnn can be improved remarkably by using our method. by means of this...

The relation existing between support vector machines (SVMs) and recurrent associative memories is investigated. The design of associative memories based on the generalized brain-state-in-a-box (GBSB) neural model is formulated as a set of independent classification tasks, which can be efficiently solved by standard software packages for SVM learning. Some properties of the networks designed in...

In this paper, an image restoration algorithm is proposed to identify nonlinear and noncausal blur funclon using artificial neural networks. Image and degradation processes include both linear and nonlinear phenomena. The proposed neural network model, which combines an adaptive auto-associative network with a random Gaussian process, is used to restore the blurred image and blur function, simu...

⎯By employing the Lyapunov stability theory and linear matrix inequality (LMI) technique, delaydependent stability criterion is derived to ensure the exponential stability of bi-directional associative memory (BAM) neural networks with time-varying delays. The proposed condition can be checked easily by LMI control toolbox in Matlab. A numerical example is given to demonstrate the effectiveness...

We apply evolutionary computations to Hop eld model of associative memory. Although there have been a lot of researches which apply evolutionary techniques to layered neural networks, their applications to Hop eld neural networks remain few so far. Previously we reported that a genetic algorithm using discrete encoding chromosomes evolves the Hebb-rule associative memory to enhance its storage ...

A useful relationship between some associative neural networks and programmable logic arrays (PLA) is discussed. The shown analogy helps to understand the properties of this class of neural networks as extensions of the properties of PLA’s.

Most models of neural associative memory have used networks with broad connectivity. However, from both a neurobiological viewpoint and an implementation perspective, it is logical to minimize the length of inter-neural connections and consider networks whose connectivity is predominantly local. The `small-world networksa model described recently by Watts and Strogatz provides an interesting ap...

A context-based attention task is employed in this paper. An Elman-type recurrent neural network is utilized to extract and keep the context information, and only the reinforcement signal that indicates whether the answer is correct or not is given. Through this learning, the function of an associative memory is observed in the Elman-type neural network. Adaptive formation of the basins are exa...