Features of pattern data can be expressed in a more informative feature domain in order to improve the classification performance. This paper proposes a new implementation of the multi-layer feed-forward neural network that can do classification based on the frequency features extracted by the first hidden layer that performs correlational filter operation. The correlational feature extraction ...

We study the learning and generalization capacity of layered feed-forward neural networks in the context of mappings of arbitrarily long bit strings into one of three ordered outputs. Many signal-processing applications reduce to this problem. We use the back-propagation learning algorithm to train the network. We describe these mappings in terms of collections of linear partitions of the input...

A three layer feed forward neural network was constructed and tested to analyze the scheduling process on single machine. The operating variables studied are the operation, processing time, setup time, deadline time, duedate time, priority, machine, and fabric color. These variables were used as input to the constructed neural network in order to predict the scheduling completion time as the ou...

In a 'feed forward' algorithm, the slope of the activation function is directly influenced by a parameter referred to as 'gain'. In this paper, the influence of the variation of 'gain' on the learning ability of a neural network is analysed. Multi layer feed forward neural networks have been assessed. Physical interpretation of the relationship between the gain value and learning rate and weigh...

In this paper, critical conditions in electric power systems are monitored by applying various neural networks. In order to accomplish the stated goal, the authors tried several combinations of Feed Forward Neural Network and Layer Recurrent Neural Networks by imparting appropriate training schemes through supervised learning in order to formulate a comparative analysis on their performance. On...

We extend here a general mathematical model for feed-forward neural networks. Such a network is represented as a vectorial function f of two variables, x (the input of the network) and w (the weight vector). We have already shown that the differential of f can be computed with an extended back-propagation algorithm as well as with a direct method. In this paper, we show that the second differen...

Production of highly viscous tar sand bitumen using Steam Assisted Gravity Drainage (SAGD) with a pair of horizontal wells has advantages over conventional steam flooding. This paper explores the use of Artificial Neural Networks (ANNs) as an alternative to the traditional SAGD simulation approach. Feed forward, multi-layered neural network meta-models are trained through the Back-Error-Propaga...

ABSTRACT Optimization algorithms are normally influenced by metaheuristic approach. In recent years several hybrid methods for optimization are developed to find out a better solution. The proposed work using meta-heuristic Nature Inspired algorithm is applied with back-propagation method to train a feedforward neural network. Firefly algorithm is a nature inspired meta-heuristic algorithm, and...