Rainfall Estimation Using Neuron-Adaptive Higher Order Neural Networks

Introduction

Artificial Higher Order Neural Network (HONN) models are the trends for emerging applications in the computer science and engineering areas. An, Mniszewski, Lee, Papcun, and Doolen (1988A and 1988B) test a learning procedure (HIERtalker), based on a default hierarchy of high order neural networks, which exhibited an enhanced capability of generalization and a good efficiency to learn to read English aloud. HIERtalker learns the `building blocks' or clusters of symbols in a stream that appear repeatedly. Salem and Young (1991) study the interpreting line drawings with higher order neural networks. A higher order neural network solution to line labeling is presented. Line labeling constraints in trihedral scenes are designed into a Hopfield-type network. The labeling constraints require a higher order of interaction than that of Hopfield-type network. Liou and Azimi-Sadjadi (1993) present a dim target detection using high order correlation method. This work presents a method for clutter rejection and dim target track detection from infrared (IR) satellite data using neural networks. A high-order correlation method which recursively computes the spatio-temporal cross-correlations is used. Liatsis, Wellstead, Zarrop, and Prendergast (1994) propose a versatile visual inspection tool for the manufacturing process. The dynamically changing nature and the complex behavior of processes in manufacturing cells dictate the need for lean, agile and flexible manufacturing systems. Tseng and Wu (1994) post Constant-time neural decoders for some BCH codes. High order neural networks are shown to decode some BCH codes in constanttime with very low hardware complexity. HONN is a direct extension of the linear perceptron: it uses a polynomial consisting of a set of product terms as its discriminant. Zardoshti-Kermani and Afshordi (1995) try classification of chromosomes by using higher-order neural networks. In this paper, the application of a higher-order neural network for the classification of human chromosomes is described. The higher order neural network's inputs are 30-dimensional feature space extracted from chromosome images. Starke, Kubota, and Fukuda (1995) research combinatorial optimization with higher order neural networks-cost oriented competing processes in flexible manufacturing systems. Higher order neural networks are applied to handle combinatorial optimization problems by using cost oriented competing processes (COCP). This method has a high adaptability to complicated problems. Miyajima, Yatsuki, and Kubota (1995) study the dynamical properties of neural networks with product connections. Higher order neural networks with product connections which hold the weighted sum of products of input variables have been proposed as a new concept. In some applications, it is shown that they are more superior in ability than traditional neural networks. Wang (1996) researches the suppressing chaos with hysteresis in a higher order neural network. Artificial higher order neural networks attempt to mimic various features of a most powerful computational system-the human brain.