Three-dimensional (3-D) objects recognition and localization is of major importance in a wide range of applications. A number of implementations concerning the complex 3D object recognition and localization have been achieved using some heuristic approaches. However, theoretical considerations concerning the construction of invariant (or quasi invariant) relations between the types of ojbect (identifying those objects) and the position in 3D space of those objects is still a problem. That is why, a marker based method for recognition and localization of 3D objects from their 2D image is suggested in this paper. Theorems relative to the features and conditions of such markers are proposed and demonstrated. Examples are given and discussed.
KEYWORDS: Neural networks, Prototyping, Manufacturing, Semiconducting wafers, Data processing, Modeling, Very large scale integration, Image processing, Signal processing, Genetic algorithms
Prediction and modeling in the case of non linear systems (or processes), especially of complex industrial processes are known being a class of involved problems. In this paper, we deal with the production yield prediction dilemma in VLSI manufacturing. An RBF neural networks based approach and its hardware implementation on a ZISC neural board have been presented. Experimental results comparing our approach with an expert have been reported and discussed.
KEYWORDS: Digital signal processing, Neural networks, Control systems, Device simulation, Adaptive control, Process control, Intelligence systems, System identification, Computer simulations, Electromagnetism
One of the most important problems, for a machine control process is the system identification. To identify varying parameters which are dependent from other system's parameters (speed, voltage and currents, etc.), one must have an adaptive control system. Synchronous machines conventional vector control's implementation using PID controllers have been recently proposed presenting the best actual solution. It supposes an appropriated model of the plant. But real plant's parameters vary and the P.I.D. controller is not suitable because of the parameters variation and non-linearity introduced by the machine's physical structure. In this paper, we present an on-line dynamic adaptive neural based vector control system identifying the motor's parameters of a synchronous machine. We present and discuss a DSP based real- time implementation of our adaptive neuro-controller. Simulation and experimental results validating our approach have been reported.
A very large number of works concerning the area of Artificial Neural Networks (ANN) deal with implementation of these models, especially as digital or analogue CMOS integrated circuits. All of the presented implementations of A.N.N. have been supposed to be working in ideal conditions but real applications will be subject to global perturbations. Unfortunately, very few papers analyze the behavior of analogue implementation of neural network with such kind of perturbations. Since 1994, we have investigated the behavior modeling of electronic A.N.N. with global perturbation conditions. We have scrutinized the behavior analysis of a CMOS analogue implementation of synchronous Boltzmann Machine model with both ambient temperature and electrical perturbations (supply voltage) perturbation. In this paper we present, using our model, the analysis of these global perturbations effects on learning capability in a CMOS analogue implementation of synchronous Boltzmann Machine Simulation and experimental results have been exposed validating our concepts.
In this paper, we present a neural network based method that allows the optimal selection of a data fusion policy. We build dynamically the internal layer of a functional link network (FLN), we add to the classical FLN, a pruning algorithm, that allows to find the optimal architecture of the FLN and to define an optimal fusion policy. In order to use the FLN as a universal fusion operator, the functional expansion performed by its internal layer includes fusion operators. As the FLN minimize the mean square error (MSE) during the learning step, an optimal fusion policy is reached in the sense of the MSE. Some academic simulations validate our approach.
One of classes of stochastic algorithms, which are very powerful in the case of the degraded image reconstruction of a degraded image using iterative stochastic process require a large number of operations. We are investigating in massively parallel implementation of image processing dedicated simulated annealing based algorithms. In this paper, we discuss compromises for such implementation based on study of two dynamics: Metropolis and Glauber ones. Simulated results have been reported.
The use of neurocomputing to solve real world problems is some times penalized by the long computing time, especially in the training phase of the neural network. A lot of approaches were suggested in the literature that allows to reduce the computing time and to enhance the generalization factor. The approach we propose in this paper is original by the fact that it is based on the following paradigm: Divide To Simplify or DTS. The task of learning is decomposed in 2 steps. In the first step, we analyze the set of the input patterns and decompose the input space in several regions of interest. This is done by using an unsupervised prototype based neural network: a Kohonen Self Organized feature Map (SOM). The second step consists of training several feature based networks to learn the behavior of each region of interest. In this way, we obtain a set of specialized neural networks. We will present in this paper various data driven methods that use the DTS paradigm to build efficient Multi- Neural Networks systems. The efficiency of this context means fast learning time, fast execution or relaxation time, enhanced generalization factor and intrinsic implementation on a parallel machine. Our approach has been used, with success, in an industrial real world process control to analyze such a complex problem with multi-variable data.
Comparing to the progress accomplished in the area of digital circuits and systems, the analogue circuits fault diagnosis field is still in its infancy. Recently, some approaches to analog circuit's fault diagnosis have been proposed using pattern recognition capability of artificial neural networks. However, the major of these papers have analyzed linear analog circuits including resistors exclusively. In this paper, we present several neural network based approaches to analog circuits fault diagnosis using Back-Propagation, Learning Vector Quantization and Radial Basis Function neural models. The interest of our approaches is related to the fact that we use competitive multi-neural network architecture. Case study, simulation results and experimental validation of presented techniques have been reported.
In 1995 we have presented a new approach to intelligent control based on Restricted Coulomb Energy concept which derives from Radial Basis Function (RBF) like neural network. A parallel implementation of adaptive process control using this technique has been discussed. In this paper we present a Zero Instruction Set Computer based parallel implementation of the real time intelligent adaptive control (using RBF like neural network). We expose the architecture of our parallel neural controller discussing the neurons consumption (use up) in learning phase and it's capabilities in the operation phase (relaxation). Analysis of feature space (parameter space) mapping obtained from experimental validation and experimental results are presented validating our concepts.
Analogue implementation of Artificial Neural Networks (ANN) especially as CMOS integrated circuits show several attractive features. During the last decade, numerous works show that small size analogue ANN operate correctly. However, today the efforts are focused on real industrial size application of ANN that will require large networks. On the other hand, all of the presented implementations of ANN have been supposed to be working in ideal conditions but real applications will be subject to some global perturbations. Especially in the case of the analogue and mixed digital/analogue implementation, the behavior analysis of the neural network with perturbation conditions is thus inevitable. Unfortunately, very few papers analyze the behavior of analogue neural network with global perturbations. We have investigated modeling and experimental validation of the behavior of analogue ANN in the case of a global perturbation of the network. We have analyzed the behavior of a CMOS analogue implementation of synchronous Boltzmann Machine model when the neural circuit is subject to perturbations. The perturbations we have considered concern the supply voltage of the neural circuit and ambient temperature in which the circuit operates. In this paper we present the analysis of the behavior of the analogue implementation of synchronous Boltzmann Machine with electrical and thermal perturbations. Simulation and experimental results have been exposed.
KEYWORDS: Process control, Signal processing, Neural networks, Image processing, Algorithm development, Digital signal processing, Process modeling, Neurons, Human-machine interfaces, Position sensors
Most of applications on neural adaptive process control are developed on back-propagation or CMAC algorithms. We present here a new approach based on a derivative of Radial Basis Function Network: The Restricted Coulomb Energy (RCE) for a parallel implementation of adaptive process control. The RCE network has been implemented on a single board based on the Zero Instruction Set Computer (ZISC-036) neural processor of IBM. The network learning consists on identification of a real second order process (DC motor with position sensor). We expose the learning and generalization phases of network, then we give simulation and experimental results.
It is now well known and well accepted that stochastic algorithms are very powerful in the case of degraded image reconstruction. One of the classes of such an approach is simulated annealing based algorithms. However, the reconstruction of a degraded image using iterative stochastic process requires a large number of operations. We are investigating massively parallel implementation of image processing dedicated simulated annealing based algorithms. In this paper, we discuss a massively parallel implementation of Carnevali's algorithm. We debate the elementary processor's structure of such implementation.
The implementation of artificial neural networks (ANN) as CMOS analog integrated circuits shows several attractive features. Stochastic models, especially the Boltzmann Machine, show a number of many attractive features. Recent studies on artificial models point out that classification is their most successful application field, and that real pattern recognition tasks, and especially image processing by artificial neural networks will require large networks. All of the presented implementations of ANN are supposed to be working in ideal conditions but real applications are subject to perturbations. For a digital implementation of ANN perturbation effects could be neglected in a firth order approximation. But for the analog and mixed digital/analog implementation cases, the behavior analysis of the neural network with perturbation conditions is inevitable. Unfortunately, very few papers analyze the behavior of analog neural networks with perturbation or their limitations. In this paper we present the analysis of a Boltzmann Machine model's behavior with physical temperature perturbation. The relation between the T parameter of the Boltzmann Machine model and the physical temperature of circuit has been established. Simulation results are presented and temperature effects compensation is discussed.
The implementation of artificial neural networks (ANN) as CMOS analog integrated circuits shows several attractive features. Stochastic models, and especially the Boltzmann Machine shows a number of many attractive features. Numerous papers show that small size analog networks operate correctly. However, recent studies on artificial models point out that classification is their most successful application field: so real pattern recognition tasks will require large networks. On the other hand, all of the presented implementations of ANN have been supposed to be working in ideal conditions but real applications will subject to perturbations. For a digital implementation of ANN perturbation effects could be neglected in a fifth-order approximation. But for the analog and mixed digital/analog implementation cases, the behavior analysis of the neural network with perturbation conditions is inevitable. Unfortunately, very few papers analyze the behavior of analog neural networks with perturbation or their limitations. In this paper we present the analysis of a CMOS analog implementation of synchronous Boltzmann Machine model's behavior with physical temperature perturbations. The relation between the T parameter of the Boltzmann Machine's model and the physical temperature of circuit has been presented. Simulation results have been given, temperature effects compensation have been discussed, and experimental results have been exposed.
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