Efficient indexing schemes have influenced the acceptance of production systems in the industrial world. However, in embedded-control systems, production systems have not been applied intensively because of their nondeterministic run-time behavior. Thus, nonpredictability of response times is a major obstacle to the widespread use of expert systems in the real-time domain. The RETE and TREAT algorithms and their offspring play a major role in the implementation of efficient pattern-matching systems.

(Stankovic and Ramamithram 1990). Such systems are considered intelligent switching that is used in the railway stations when they are able to perform complex of three European countries. Parts of the control actions in response to the sensed environment. Time is a valuable resource that is lost when Years ago, a request came from the railway the system must reason about actions before authorities for a system that could guarantee performing them. This requirement is understandable on two levels: the problem-space level because in typical real-time systems, and the knowledge base level (Tambe and scheduling is mostly based on worstcase Newell 1988). With the problem-space level, execution times of the tasks involved. Usually, a sequence of steps is required the railway authorities.

In this work we apply a texture classification network to remote sensing image analysis. The goal is to extract the characteristics of the area depicted in the input image, thus achieving a segmented map of the region. We have recently proposed a combined neural network and rule-based framework for texture recognition. The framework uses unsupervised and supervised learning, and provides probability estimates for the output classes. We describe the texture classification network and extend it to demonstrate its application to the Landsat and Aerial image analysis domain. 1 INTRODUCTION In this work we apply a texture classification network to remote sensing image analysis. The goal is to segment the input image into homogeneous textured regions and identify each region as one of a prelearned library of textures, e.g.

In this work we apply a texture classification network to remote sensing image analysis. The goal is to extract the characteristics of the area depicted in the input image, thus achieving a segmented map of the region. We have recently proposed a combined neural network and rule-based framework for texture recognition. The framework uses unsupervised and supervised learning, and provides probability estimates for the output classes. We describe the texture classification network and extend it to demonstrate its application to the Landsat and Aerial image analysis domain. 1 INTRODUCTION In this work we apply a texture classification network to remote sensing image analysis. The goal is to segment the input image into homogeneous textured regions and identify each region as one of a prelearned library of textures, e.g.

We demonstrate in this paper how certain forms of rule-based knowledge can be used to prestructure a neural network of normalized basis functions and give a probabilistic interpretation of the network architecture.

We demonstrate in this paper how certain forms of rule-based knowledge can be used to prestructure a neural network of normalized basisfunctions and give a probabilistic interpretation of the network architecture. We describe several ways to assure that rule-based knowledge is preserved during training and present a method for complexity reduction that tries to minimize the number ofrules and the number of conjuncts. After training the refined rules are extracted and analyzed.

In this work we apply a texture classification network to remote sensing image analysis.The goal is to extract the characteristics of the area depicted in the input image, thus achieving a segmented map of the region. We have recently proposed a combined neural network and rule-based framework for texture recognition. The framework uses unsupervised and supervised learning, and provides probability estimates for the output classes. We describe the texture classification network and extend it to demonstrate its application to the Landsat and Aerial image analysis domain. 1 INTRODUCTION In this work we apply a texture classification network to remote sensing image analysis. The goal is to segment the input image into homogeneous textured regions and identify each region as one of a prelearned library of textures, e.g.

PITCH EXPERT was developed to make expertise available to mill-site engineers to solve pitch problems in kraft pulp mills. These problems have been estimated to cause losses to the Canadian pulp and paper industry in excess of $80 million each year. The design of the system took into account not only the complexity of the process interactions and the need for accuracy and completeness of recommendations but also the ongoing need for training mill personnel and the requirement that the system be maintainable and expandable without the constant involvement of the developers. PITCH EXPERT is now accessible by modem, and the savings achieved through use of the system covered the development costs within six months of release.

Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it -- What is it? -- has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important to the notion of representation in general. In this article, we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and, at times, conflicting demands on the properties a representation should have. We argue that keeping in mind all five of these roles provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field.

A combined neural network and rule-based approach is suggested as a general framework for pattern recognition. This approach enables unsupervised and supervised learning, respectively, while providing probability estimates for the output classes. The probability maps are utilized for higher level analysis such as a feedback for smoothing over the output label maps and the identification of unknown patterns (pattern "discovery"). The suggested approach is presented and demonstrated in the texture - analysis task. A correct classification rate in the 90 percentile is achieved for both unstructured and structured natural texture mosaics. The advantages of the probabilistic approach to pattern analysis are demonstrated.