Image Compression using Artificial Neural Networks is a topic where research is being carried out in various directions towards achieving a generalized and economical network. Feedforward Networks using Back propagation Algorithm adopting the method of steepest descent for error minimization is popular and widely adopted and is directly applied to image compression. Various research works are d...

In this study a back-propagation neural network model is designed and its parameters are optimized for prediction of horoscope to identify a person type. Person type is a dynamic system based on the planet system. It is found that the backpropagation neural network is capable to predict the person type by learning planet dataset. The model is trained up to model error (i.e., mean square error) ...

Financial forecasting is an example of signal processing problems. A number of ways to train/learn the network are available. We have used Levenberg-Marquardt algorithm for error back-propagation for weight adjustment. Pre-processing of data has reduced much of the variation at large scale to small scale, reducing the variation of training data. Keywords— Gradient descent method, jacobian matri...

The two well known learning algorithms of recurrent neural networks are the back-propagation (Rumelhart & el al., Werbos) and the forward propagation (Williams and Zipser). The main drawback of back-propagation is its off-line backward path in time for error cumulation. This violates the on-line requirement in many practical applications. Although the forward propagation algorithm can be used i...

We propose that the back propagation algorithm for supervised learning can be generalized, put on a satisfactory conceptual footing, and very likely made more efficient by defining the values of the output and input neurons as probabilities and varying the synaptic weights in the gradient direction of the log likelihood, rather than the 'error'. In the past thirty years many researchers have st...

Abstract Thermal errors are one key impact factor on the processing accuracy of numerical control machine. This study targeted at a certain vertical center presents new algorithm for predictive modeling thermal in is founded back-propagation neural networks (BPNNs) and adopts beetle antennae search (BAS) to find best weights thresholds BPNNs. It avoids local minimization due extremums faced by ...

The Adaptive Solutions CN APS architecture chip is a general purpose neurocomputer chip. It has 64 processors, each with 4 K bytes of local memory, running at 25 megahertz. It is capable of implementing most current neural network algorithms with on chip learning. This paper discusses the implementation of the Back Propagation algorithm on an array of these chips and shows performance figures f...