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2 Image Processing & Communications

2 2005

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Self-organizing multi-model based identification: application to nonlinear dynamic systems' behavior prediction

Thiaw L., Madani K.

Image Processing & Communications

2005

Vol. 10, no 2

63-74

Recently, multiple works proposed multi-model based approaches to model nonlinear systems. Such approaches could also be seen as some specific approach, inspired from Artificial Neural Network's operation mode, where each neuron, represented by one of the local models, realizes some higher level transfer function. We are involved in nonlinear dynamic systems identification and behavior prediction, which are key steps in several areas of industrial applications. In this paper, two multi-model based identifiers architectures with self-organization capability are presented, in the frame of nonlinear system's, behavior prediction context. Experimental results validating presented multi-model based structures have been reported and discussed.

Hybrid intelligent classification for Computer Aided Diagnosis (CAD) systems using image representation

Chochra A., Kanaoui N., Madani K.

Image Processing & Communications

2005

Vol. 10, no 2

7-16

In this paper, a neural hybrid image classification for intelligent diagnosis systems from signal to image conversion (image representation) is suggested. Such hybrid approach (multiple models) mainly aims to ensure a satisfactory reliability for faults diagnosis systems, and particularly for medical diagnosis. Thus, an overview is given on how neural global and local approximators are interesting for image classification and why image classification (from signals to images conversion) is efficient than signal classification for fault diagnosis systems. Then, a neural hybrid image classification approach is suggested for intelligent medical diagnosis help, from biomedical signals, using MLP and RBF networks, under supervised learning. In this approach, each image is divided in several sub-images (local indicators) which are classified by global approxirnators (MLP) and by local approxirnators (RBF). Afterwards, a fuzzy decision-making system is suggested to give the final diagnosis with a Confidence Index (CI) parameter.