In associative memory, recall is based on similarity to a cue. With its inherent data parallelism, associative memory naturally lends itself to implementation on massively parallel hardware; it is our thesis that associative processing can serve as the basis of AI systems. We believe that the associative paradigm can encompass both neural network and symbolic applications. Current research indi...

There have been a lot of researches which apply evolutionary techniques to layered neural networks. However, their applications to Hop eld neural networks remain few so far. We have been applying a genetic algorithm to fully connected associative memory model of Hop eld, and reported elsewhere that the network can store some number of patterns only by evolving weight matrices with the genetic a...

Associative storage and retrieval of binary random patterns in various neural net models with one-step threshold-detection retrieval and local learning rules are the subject of this paper. For diierent hetero-association and auto-association memory tasks, speciied by the properties of the pattern sets to be stored and upper bounds on the retrieval errors, we compare the performance of various m...

This paper look at how the Hopfield neural network can be used to store and recall patterns constructed from natural language sentences. As a pattern recognition and storage tool, the Hopfield neural network has received much attention. This attention however has been mainly in the field of statistical physics due to the model’s simple abstraction of spin glass systems. A discussion is made of ...

We propose a biologically-inspired auto/heteroassociative spiking neural network combined with a working memory model, in which a state-driven forward sequence and a goal-driven backward sequence on the associative network are integrated on the working memory to make a plan. By discrete pulse-driven neural network simulations, we show that several characteristics of planning process such as goa...

Abstract—In this paper, a class of generalized bi-directional associative memory (BAM) neural networks with mixed delays is investigated. On the basis of Lyapunov stability theory and contraction mapping theorem, some new sufficient conditions are established for the existence and uniqueness and globally exponential stability of equilibrium, which generalize and improve the previously known res...

This paper deals with the problem of delay-dependent asymptotically stability for stochastic bidirectional associative memory neural networks with time-varying structured uncertainties and time-varying delays. The parameter uncertainties are assumed to be norm bounded. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, new delay-dependent stability criteria ...

In this paper, we explore the recognition of polyphone. The cognition process is complex, which needs other additional information, otherwise it may cause uncertainty in decision. Recent research is almost focused on phonetics, while we plan to explore the question with neural networks. H. Haken used synergetic neural network to discuss the recognition of ambivalent patterns and the evolution e...

Multi cast communication is a key technology for wireless mesh networks. Multicast provides efficient data distribution among a group of nodes, Generally sensor networks and MANETs uses multicast algorithms which are designed to be energy efficient and to achieve optimal route discovery among mobile nodes whereas wireless mesh networks needs to maximize throughput. Here we propose two multicast...