While individual neurons function at relatively low firing rates, naturally-occurring nervous systems not only surpass manmade systems in computing power, but accomplish this feat using relatively little energy. It is asserted that the next major breakthrough in computing power will be achieved through application of neuromorphic approaches that mimic the mechanisms by which neural systems inte...

The development of soft computing methods has attracted considerable research interest over the past decade. They are applied to important fields such as control, signal processing, and system modeling. Although soft computing methods have shown great potential in these areas, they share some common shortcomings that hinder them from being used more widely. For example, neural networks, a compo...

one of the main features of high throughput computing systems is the availability of high power processing resources. cloud computing systems can offer these features through concepts like pay-per-use and quality of service (qos) over the internet. many applications in cloud computing are represented by workflows. quality of service is one of the most important challenges in the context of sche...

The NeuroWeb project is an Internet-based framework for the simulation of neural networks. It aims for using the Internet as a transparent environment to allow users the exchange of information (neural network objects, neural network paradigms) and the exploit of available computing resources for neural network specific tasks (specifically training of neural networks). NeuroWeb’s design princip...

Background: Cloud computing, as a technology developed under the rapid development of modern network, is mainly used for processing large-scale data. The traditional data mining algorithms such as neural network algorithm are usually used for processing small-scale data. Therefore, the calculation of large-scale data using neural network algorithm must be based on cloud computing. Materials and...

This paper gives an overview and considers impacts of some theories developed around uncertainty representation paradigms, theory of neural networks and of evolutive computing on development of computing systems. Possible further development of soft computing and impacts of that development on the development of computing systems in general, is considered.

Introduction: Even before Artificial Intelligence was its own field of computational science, men have tried to mimic the activity of the human brain. In the early 1940s the first artificial neuron models were created as purely mathematical concepts. Over the years, ideas from neuroscience and computer science were used to develop the modern Neural Network. The interest in these models rose qui...

Soft computing techniques are becoming popular in designing real world applications. Researchers are trying to integrate different soft computing paradigms such as fuzzy logic, artificial neural network, genetic algorithms, decision trees etc. to develop hybrid intelligent autonomous classification systems that provide more flexibility by exploiting tolerance and uncertainty of real life situat...

Three recurrent neural networks are presented for computing the pseudoinverses of rank-deficient matrices. The first recurrent neural network has the dynamical equation similar to the one proposed earlier for matrix inversion and is capable of Moore–Penrose inversion under the condition of zero initial states. The second recurrent neural network consists of an array of neurons corresponding to ...

We present the hardware implementation of partially connected neural network that is defined as an extended of the MultiLayer Perceptron (MLP) model. We demonstrate that partially connected neural networks lead to a higher performance in terms of computing speed (requiring less memory and computing resources). This work addresses a complete study that compares the hardware implementation of MLP...