This brief presents a synthesis procedure (design algorithm) for cellular neural networks with space-invariant cloning template with applications to associative memories. The design algorithm makes it possible to determine in a systematic manner cloning templates for cellular neural networks with or without symmetry constraints on the interconnection weights. Two specific examples are included ...

This paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a modelbased approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component an...

A description is given of 11 papers from the April 1990 special issue on neural networks in control systems of IEEE Control Systems Magazine. The emphasis was on presenting as varied and current a picture as possible of the use of neural networks in control. The papers described cover: the design of associative memories using feedback neural networks; a method to use neural networks to control ...

This paper presents an alternative technique for financial distress prediction systems. The method is based on a type of neural network, which is called hybrid associative memory with translation. While many different neural network architectures have successfully been used to predict credit risk and corporate failure, the power of associative memories for financial decision-making has not been...

A model of associative memory is studied, which stores and reliably retrieves many more patterns than the number of neurons in the network. We propose a simple duality between this dense associative memory and neural networks commonly used in deep learning. On the associative memory side of this duality, a family of models that smoothly interpolates between two limiting cases can be constructed...

Associative memory using fast weights is a short-term memory mechanism that substantially improves the memory capacity and time scale of recurrent neural networks (RNNs). As recent studies introduced fast weights only to regular RNNs, it is unknown whether fast weight memory is beneficial to gated RNNs. In this work, we report a significant synergy between long short-term memory (LSTM) networks...

Most models of neural associative memory have used networks with broad connectivity. However, this seems unrealistic from a neuroanatomical perspective. A simple model of associative memory with emergent properties was introduced by Hopfield [5]. We choose this widely known model to investigate the impact of connectivity on the storage capacity and the retrieval dynamics in artificial associati...

In this paper, a novel associative memory model will be proposed and applied to memory retrievals based on the conventional continuous time model. The conventional model presents memory capacity is very low and retrieval process easily converges to an equilibrium state which is very different from the stored patterns. Genetic Algorithms is well-known with the capability of global optimal search...

The recent trends in optimisation of sustainability of production processes requires, amongst all the activities, a continuous detection and correction of process behaviours, monitoring those parameters critical to performance. Detection of special causes of variations is a basic task in manufacturing, that has to be performed continuously to maintain any process stable as well as predictable. ...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...