The theoretical, practical and technical development of neural associative memories during the last 40 years is described. The importance of sparse coding of associative memory patterns is pointed out. The use of associative memory networks for large scale brain modeling is also mentioned.

A review of works on associative neural networks accomplished during last four years in the Institute of Optical Neural Technologies RAS is given. The presentation is based on description of parametrical neural networks (PNN). For today PNN have record recognizing characteristics (storage capacity, noise immunity and speed of operation). Presentation of basic ideas and principles is accentuated.

Due to remarkable capabilities of artificial neural networks (ANNs) such as generalization and nonlinear system modeling, ANNs have been extensively studied and applied in a wide variety of applications (Amiri et al., 2007; Davande et al., 2008). The rapid development of ANN technology in recent years has led to an entirely new approach for the solution of many data processing-based problems, u...

One common approach to the robot learning technique Learning From Demonstration, is to use a set of preprogrammed skills as building blocks for more complex tasks. One important part of this approach is recognition of these skills in a demonstration comprising a stream of sensor and actuator data. In this paper, three novel techniques for behavior recognition are presented and compared. The fir...

We discuss a definition of Morphological Cellular Neural Networks (MCNN) where the state change operator are Auto-associative Morphological Memories (AMM). The fast convergence properties of AMM and the shape of its fixed point set make the MCNN dynamics trivial. However, segmentation results are poor. We propose a Morphological Cellular Automata (MCA) with assured convergence to a state charac...

This article is the second part of a work dealing with the optoelectronic implementation of artificial neural networks. The authors analyze the problems involved by using computer-generated holograms (CGH) for these interconnections and some methods of designing such diffractive elements. The authors also analyze the error sources and the consequences caused by random deviations of the neurons ...

Associative networks are a connectionist language model with the ability to handle dynamic data. We used two associative networks to categorize random sets of related Wikipedia articles with only their raw text as input. We then compared the resulting categorization to a gold standard: the manual categorization by Wikipedia authors and used a neural network as a baseline. We also determined a h...

The widely used principal component analysis (PCA) is implemented in nonlinear by an auto-associative neural network. Compared to other nonlinear versions, such as kernel PCA, such a nonlinear PCA has explicit encoding and decoding processes, and the data can be transformed back to the original space. Its data compression performance is similar to that of PCA, but data analysis performance such...

Every individual has some unique speaking style and this variation influences their speech characteristics. Speakers’ native dialect is one of the major factors influencing their speech characteristics that influence the performance of automatic speech recognition system (ASR). In this paper, we describe a method to identify Hindi dialects and examine the contribution of different acoustic-phon...

this paper presents the prediction of vehicle's velocity time series using neural networks. for this purpose, driving data is firstly collected in real world traffic conditions in the city of tehran using advance vehicle location devices installed on private cars. a multi-layer perceptron network is then designed for driving time series forecasting. in addition, the results of this study a...