Mark H. Beale

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Real-world applications will inevitably entail divergence between samples on which chemometric classifiers are trained and the unknowns requiring classification. This has long been recognized, but there is a shortage of empirical studies on which classifiers perform best in 'external validation' (EV), where the unknown samples are subject to sources of(More)
In this paper, we introduce a new procedure for efficient training of recurrent neural networks. The new procedure uses a batch training method based on a modified version of the Levenberg-Marquardt algorithm. The information of gradients of individual sequences is used to mitigate the effect of spurious valleys in the error surface of recurrent networks.(More)
This work presents the implementation of trainable Artificial Neural Network (ANN) chip, which can be trained to implement certain functions. Usually training of neural networks is done off-line using software tools in the computer system. The neural networks trained off-line are fixed and lack the flexibility of getting trained during usage. In order to(More)
Parikshik dutta, Dilip Kumar pratihar do modelling of TIF welding process using conventional regression analysis and neural network-based approaches. journal of Materials Processing Technology 184 (2007) 56–68 P. Sathiya ?, K. Panneerselvam, M. Y. Abdul Jaleel "Optimization of laser welding process parameters for super austenitic stainless steel using(More)
Stock market prediction plays a vital rule in taking financial decisions. Various factors affecting the stock market makes stock prediction somewhat complex and difficult. Different data mining techniques such as Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) etc are being widely used for predicting stock prices of different(More)
Today, investment by purchasing stock-share constitutes the greater part of economic exchange of countries and a considerable amount of capital is exchanged through the stock markets in the whole world. But one of the most important problems is finding efficient ways to summarize and visualize the stock market data to individual or institutions useful(More)
The resolution of remote sensing images increase every day . Most of the existing methods is used the same method for years. The existing method does not provide satisfactory result. The aim is to develop an artificial neural network based on classification method consists of segmentation and classification . Segmentation followed by K-Means method and then(More)
AC servo systems are extensively used in robotic actuators and are competing with DC servo motors for motion control because of their favorable electrical and mechanical properties. This paper presents an approach towards the control system tuning for the speed control of an AC servo motor. An approach towards speed control of servo motor in presence of(More)
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