This paper tests a novel improvement in neural network training by implementing Metaplasticity Multilayer Perceptron for cardiac arrhythmias classification. The proposed training algorithm is inspired by the biological metaplasticity property of neurons.The plasticity property of synaptic connections in the brain is modeled in many Artificial Neural Networks as a change in the connection weight...

Determining whether two questions are asking the same thing can be challenging, as word choice and sentence structure can vary significantly. Traditional natural language processing techniques such as shingling have been found to have limited success in separating related question from duplicate questions. Using a dataset of 400,000 labeled question pairs provided by question-and-answer forum Q...

The nearest-neighbor multilayer perceptron (NN-MLP) is a single-hidden-layer network suitable for pattern recognition. To design an NN-MLP efficiently, this paper proposes a new evolutionary algorithm consisting of four basic operations: recognition, remembrance, reduction, and review. Experimental results show that this algorithm can produce the smallest or nearly smallest networks from random...

We propose a framework for object extraction with accurate boundaries. A multilayer perceptron is used to identify seed points through examples, and regions are extracted and localized using a locally coupled network with weight adaptation. A functional system has been developed and applied to hydrographic region extraction from Digital Orthophoto Quarter–Quadrangle images.

Artificial Neural Networks(ANN) has been phenomenally successful on various pattern recognition tasks. However, the design of neural networks rely heavily on the experience and intuitions of individual developers. In this article, the author introduces a mathematical structure called MLP algebra on the set of all Multilayer Perceptron Neural Networks(MLP), which can serve as a guiding principle...

Neural Networks (NNs) are capable of learning high complex, nonlinear input-output mappings. This characteristic of NNs enables them to be used in nonlinear system modeling and prediction applications. On the other hand, the wavelet decomposition provides a powerful tool for functional approximation. In this paper, a kind of Wavelet Neural Networks (WNNs) is proposed for Differential GPS (DGPS)...

A novel improvement in neural network training for pattern classification is presented in this paper. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannon’s information theory. This algorithm is applicable to artificial neural networks (ANNs) in general, although here it is applied to a multilayer perceptron (MLP). During the training phas...

abstract infiltration process is one of the most important components of the hydrological cycle. on the other hand, the direct measurement of infiltration process is laborious, time consuming and expensive. in this study, the possibility of predicting cumulative infiltration in specific time intervals, using readily available soil data and artificial neural networks (anns) was investigated. for...