Publisher Summary This chapter provides an account of different neural network architectures for pattern recognition. A neural network consists of several simple processing elements called neurons. Each neuron is connected to some other neurons and possibly to the input nodes. Neural networks provide a simple computing paradigm to perform complex recognition tasks in real time. The chapter categorizes neural networks into three types: single-layer networks, multilayer feedforward networks, and feedback networks. It discusses the gradient descent and the relaxation method as the two underlying mathematical themes for deriving learning algorithms. A lot of research activity is centered on learning algorithms because of their fundamental importance in neural networks. The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue. It closes with the discussion of performance and implementation issues.

Neural Networks for Pattern Recognition

A pattern classification method called neural tree networks (NTNs) is presented. The NTN consists of neural networks connected in a tree architecture. The neural networks are used to recursively partition the feature space into subregions. Each terminal subregion is assigned a class label which depends on the training data routed to it by the neural networks. The NTN is grown by a learning algorithm, as opposed to multilayer perceptrons (MLPs), where the architecture must be specified before learning can begin. A heuristic learning algorithm based on minimizing the L1 norm of the error is used to grow the NTN. It is shown that this method has better performance in terms of minimizing the number of classification errors than the squared error minimization method used in backpropagation. An optimal pruning algorithm is given to enhance the generalization of the NTN. Simulation results are presented on Boolean function learning tasks and a speaker independent vowel recognition task. The NTN compares favorably to both neural networks and decision trees. >

Growing and pruning neural tree networks

Neural networks were used to estimate the offset, depth, and conductivity‐area product of a conductive target given an electromagnetic ellipticity image of the target. Five different neural network paradigms and five different representations of the ellipticity image were compared. The networks were trained with synthetic images of the target and tested on field data and more synthetic data. The extrapolation capabilities of the networks were also tested with synthetic data lying outside the spatial limits of the training set. The data representations consisted of the whole image, the subsampled image, the peak and adjacent troughs, the peak, and components from a two‐dimensional (2-D) fast Fourier transform. The paradigms tested were standard back propagation, directed random search, functional link, extended delta bar delta, and the hybrid combination of self‐organizing map and back propagation. For input patterns with less than 100 elements, the directed random search and functional link networks gave ...

Location of subsurface targets in geophysical data using neural networks

An automatic off-line character recognition system for handwritten cursive Arabic characters is presented. A robust noise-independent algorithm is developed that yields skeletons that reflect the structural relationships of the character components. The character skeleton is converted to a tree structure suitable for recognition. A set of fuzzy constrained character graph models (FCCGM's), which tolerate large variability in writing, is designed. These models are graphs, with fuzzily labeled arcs used as prototypes for the characters. A set of rules is applied in sequence to match a character tree to an FCCGM. Arabic handwritings of four writers were used in the learning and testing stages. The system proved to be powerful in tolerance to variable writing, speed, and recognition rate. >

A. Rajavelu

Papers

A neural network approach to character recognition