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To extract implicit knowledge and data relationships from the audio and audio similarity measure, this paper uses the audio mining techniques. A model for audio clustering and classification technique is proposed. Neural networks are used for classifying the data. The working prototype of the Music classification system has been developed and tested in MATLAB 6.5 using the signal Processing Toolbox...
This paper presents the design, implementation, and comparative analysis of two intelligent neural network based controllers employed for nonlinear dynamic compensation and adaptive trajectory tracking of a mobile robot system. The first control law is an integration of a backstepping controller with a neural network which is designed to learn the inverse dynamic model of the robot and to compensate...
This paper presents a new control approach for nonlinear network-induced time delay systems using online reset control, neural networks, and dynamic Bayesian networks. We construct a state-feedback based nominal control to develop a linearized system model. The reset control and neural network are employed to compensate for system error due to time delay effect. Finally, we achieve probabilistic modeling...
Neural network techniques have been widely applied to areas of such as data mining, information integration and grid computing. This paper proposes a new learning algorithm based on trust region optimization theory. In the paper, the Dogleg-algorithm to obtain the valid trust region steps is presented, and a self-adjustable method with variable coefficients is given to resolve the problem of oscillatory...
The cavity model is suitable for analytical solution to Helmholtzpsilas equation of patch antennas. Neural network based solutions are suggested as alternatives to conventional cavity methods for the circular microstrip antenna. A trial solution of the differential equation is developed incorporating the boundary conditions. Then the network is trained to satisfy the differential equation.
In this paper we present how to estimate a continuous space language model with a neural network to be used in a statistical machine translation system. We report results for an Italian-English translation task obtained on a small corpus (about 150 K tokens), that can be considered a task with a lack of training data. Different word history length included in the connectionist language model (n-gram...
CAC-RD (call admission control based on reservation and diagnosis) [1] is call admission control (CAC) for UMTS (universal mobile terrestrial system) 3G networks. It is based on two schemes: channel reservation and network diagnosis. When compared to other CAC mechanisms, CAC-RD can guarantee network availability, reducing priority classes blocking and guarantying some network QoS requirements. Due...
The incremental learning system for a feature extraction unit in the character recognition system is described and experimental results are shown. The relationship between this learning system and neural networks (NN) are explained and the specifications of this method are described as an NN application. The improved version of this system which is related to the Gabor filter was tested and an accuracy...
The precise conversion of CT numbers to their mass densities is essential in dose calculation of radiotherapy for the inhomogeneity. A new neural network algorithm was presented for tissue density calibration. Artificial neural networks are used to learn the relationships between the CT numbers and their mass densities. It neither requires an accurate mathematical model nor needs any priori knowledge...
This research was developed in a greenhouse located in Mexico, in which there are big variations in temperature and relative humidity, generating production losses. Consequently a good greenhouse control tool was necessary to keep these variables inside of the optimal levels. Black box models have been applied in this greenhouse to predict temperature and relative humidity, however they fail in relative...
Building an accurate credit scoring model is very important to predict effectively the creditworthiness of new customers. Neural networks and genetic algorithm are suitable for building highly predictive credit scoring model, but the lack of transparency of these methods is a major drawback. On the other hand the main advantage of fuzzy models is their ability to describe the behavior of systems with...
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