In this paper, a generalized model of bi-directional associative memory (BAM) neural networks delays and impulses is investigated. By constructing suitable Lyapunov functional, Halanaly differential inequality and M -matrix theory, some sufficient conditions for global exponential stability of generalized BAM neural networks with delays and impulses are obtained. An examples are given to show t...

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 ...

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 ...

Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...

Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...

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 convolutional neural network is trained in auto/hetero-associative mode for reconstructing RGB components from a randomly mosaicked color image. The was shown to perform equally well when images are sampled periodically or with different random mosaic. Therefore, this model able generalize on every type of filter array. We attribute property universal demosaicking the learning statistical str...

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...

We present a fast algorithm for non-linear dimension reduction. The algorithm builds a local linear model of the data by merging PCA with clustering based on a new distortion measure. Experiments with speech and image data indicate that the local linear algorithm produces encodings with lower distortion than those built by five layer auto-associative networks. The local linear algorithm is also...