Space Vector Modulation (SVM) is an optimum Pulse Width Modulation (PWM) technique for an inverter used in a variable frequency drive applications. It is computationally rigorous and hence limits the inverter switching frequency. Increase in switching frequency can be achieved using Neural Network (NN) based SVM, implemented on application specific chips. This paper proposes a neural network ba...

numerous studies yet have been carried out on downscaling of the large-scale climate data usingboth dynamical and statistical methods to investigate the hydrological and meteorological impacts of climatechange on different parts of the world. this study was also conducted to investigate the capability of feedforwardneural network with error back-propagation algorithm to downscale the provincial...

The k-nearest neighbors classifier is one of the most widely used methods of classification due to several interesting features, such as good generalization and easy implementation. Although simple, it is usually able to match, and even beat, more sophisticated and complex methods. However, no successful method has been reported so far to apply boosting to k-NN. As boosting methods have proved ...

The k-nearest neighbor (k-NN) decision rule is the basis of a well-established, high-performance pattern-recognition technique but its sequential implementation is inherently slow. More recently, feedforward neural networks trained on error backpropagation have been widely used to solve a variety of pattern-recognition problems. However, it is arguably unnecessary to learn such a computationall...

Abstract In conventional approach for VLSI, implementing large numbers of operations in parallel is possible with NN. The fundamental task a neural network hardware not dependent on the implementation technology and are quite constructive simulating digital circuits. These NN representations can be incorporated various applications where behaviour these circuits essential to get solution discre...

The paper describes and improves on a Boolean neural network (NN) fan-in reduction algorithm, with a view to possible VLSI implementation of NNs using threshold gates (TGs). Constructive proofs are given for: (i) at least halving the size; (ii) reducing the depth from O(N) to O(logN). Lastly a fresh algorithm which reduces the size to polynomial is suggested.

The paper describes and improves on a Boolean neural network (NN) fan-in reduction algorithm, with a view to possible VLSI implementation of NNs using threshold gates (TGs). Constructive proofs are given for: (i) at least halving the size; (ii) reducing the depth from O(N) to O(logN). Lastly a fresh algorithm which reduces the size to polynomial is suggested.

the biologists now face with the masses of high dimensional datasets generated from various high-throughput technologies, which are outputs of complex inter-connected biological networks at different levels driven by a number of hidden regulatory signals. so far, many computational and statistical methods such as pca and ica have been employed for computing low-dimensional or hidden representat...