Nine-month-old infants can distinguish the word-stress patterns of two artificial languages after a few minutes of exposure to words from one of the languages, apparently by making transitive inferences from known word-stress constraints to unknown constraints. We report on a neuralnetwork simulation of these data using the sibling-descendant cascade-correlation algorithm. The simulations cover...

The detection and disposal of anti-personnel land mines is one of the most difficult and intractable problems faced in ground conflict. This paper presents detection methods which use a separated-aperture microwave sensor and an artificial neural-network pattern classifier. Several data-specific preprocessing methods are developed to enhance neuralnetwork learning. In addition, a generalized Ka...

This paper deals with robust flux observation of an induction motor using both an analytical observer and an artificial intelligence based one. Each developed observer is used in the direct field oriented control scheme of a 30kW induction machine. The effectiveness of the proposed schemes is checked via simulations on an induction motor driven by a space vector voltage-source pulse width modul...

Powered by new advances in sensor development and artificial intelligence, the decreasing cost of computation, pervasiveness handheld computation devices, biometric user authentication (and identification) is rapidly becoming ubiquitous. Modern approaches to authentication, based on sophisticated machine learning techniques, cannot avoid storing either trained-classifier details or explicit dat...

Model-based testing for real-life software systems often require a large number of tests, all of which cannot exhaustively be run due to time and cost constraints. Thus, it is necessary to prioritize the test cases in accordance with their importance the tester perceives. In this paper, this problem is solved by improving our given previous study, namely, applying classification approach to the...

Conventional schemes of fast valving generate a fixed valve stroke sequence for the control of turbine valves under transient conditions. A simple fixed valve control sequence cannot give optimum result for different fault conditions and loading levels, due to its poor adaptability. This paper presents an artificial-neuralnetwork (ANN) based controller to govern the operation of the turbine con...

In this paper, using the three-phase induction motor fifth order model in a stationary twoaxis reference frame with stator current and rotor flux as state variables, a conventional backsteppingcontroller is first designed for speed and rotor flux control of an induction motor drive. Then in orderto make the control system stable and robust against all electromechanical parameter uncertainties a...

This study was conducted to investigate the prediction of growth performance using linear regression and artificial neural network (ANN) in broiler chicken. Artificial neural networks (ANNs) are powerful tools for modeling systems in a wide range of applications. The ANN model with a back propagation algorithm successfully learned the relationship between the inputs of metabolizable energy (kca...

Artificial neural networks (ANN’s), due to their inherent parallelism, offer an attractive paradigm for implementation of symbol processing systems for applications in computer science and artificial intelligence. This paper explores systematic synthesis of modular neural-network architectures for syntax analysis using a prespecified grammar—a prototypical symbol processing task which finds app...

This paper proposes a novel neural-network-based adaptive hybrid-reflectance three-dimensional (3-D) surface reconstruction model. The neural network combines the diffuse and specular components into a hybrid model. The proposed model considers the characteristics of each point and the variant albedo to prevent the reconstructed surface from being distorted. The neural network inputs are the pi...