Neural networks learning systems

Other approaches Hamming networks, pattern recognition, wavelets, and neural network learning systems are sometimes discussed but have not been commercially implemented. 

R M 2000, the Air Force Reliability and Maintainability Action Plan does not discuss the unique problems (and potential) of expert systems, particularly those which rely on the massive "knowledge bases" which can now be delivered on optical disc. Nor have they addressed the problems (and potential) associated with parallel processing, neural networks and "systems that learn". 

The component techniques of soft computing are not competitive, but complementary. Much research has been done to study the ways this complementarity can be exploited. Each of the components has features to offer a potential partnership. Systems that have such a partnership are called hybrid systems . Fuzzy logic uses the concept of computing with words, it deals with imprecision and information granularity and is an important tool for approximate reasoning. Neural networks learn and adapt. Genetic algorithms make use of a systemized random search and are an important tool for optimization. These three may be combined in different ways, as described below. 

The neural network is capable of modeling non-linear systems. On the basis of supplied training data the neural network learns (trains) the relationship between the process input and output The data have to be examined carefully before they can be used as a training set for a neural network. The training sets consist of one or more input data and one or more output data. After the training of the network, a test-set of data should be used to verify whether the desired relationship was learned. 

Eor a number of cognitive or interpretive tasks, there are alternatives to mainstream knowledge-based systems that may be more appropriate, especially if adaptive behavior and learning capabihty are important to system performance. Two approaches that embody these characteristics are neural networks (nets) and case-based reasoning. 

A. Sankar and R. Mammone, A fast learning algorithm for tree neural networks. In Proc. 1990 Conf. on Information Sciences and Systems, Princeton, NJ, 1990, pp. 638-642. 

Classifier systems are software tools that can learn to control or interpret complex environments without help from the user. This is the sort of task to which artificial neural networks are often applied, but both the internal structure of a classifier system and the way that it learns are very different from those of a neural network. The "environment" that the classifier system attempts to learn about might be a physical entity, such as a biochemical fer-mentor, or it might be something less palpable, such as a scientific database or a library of scientific papers. 

A helpful starting point for further investigation is Learning Classifier Systems From Foundations to Applications.1 The literature in classifier systems is far thinner than that in genetic algorithms, artificial neural networks, and other methods discussed in this book. A productive way to uncover more... 

To learn the characteristic properties of the biochemical systems considered in this study and to assess their ability to perform as ANNs, a direct comparison between the two is made here. In so doing it should be noted that there is no universally accepted definition of an artificial neural network. Therefore, we refer here to the characteristics of ANNs summarized from some of the definitions available in the literature [17-22]. The next step is to examine if the characteristics mentioned above can also be found in the biochemical networks proposed in this study. These characteristics are compared one by one in Table 5.2. 

A neural-network-based simulator can overcome the above complications because the network does not rely on exact deterministic models (i.e., based on the physics and chemistry of the system) to describe a process. Rather, artificia] neural networks assimilate operating data from an industrial process and learn about the complex relationships existing within the process, even when the input-output information is noisy and imprecise. This ability makes the neural-network concept well suited for modeling complex refinery operations. For a detailed review and introductory material on artificial neural networks, we refer readers to Himmelblau (2008), Kay and Titterington (2000), Baughman and Liu (1995), and Bulsari (1995). We will consider in this section the modeling of the FCC process to illustrate the modeling of refinery operations via artificial neural networks. 

A state of consciousness depends on the intact function of the complex neural networks that underlie alertness, learning and memory. General anesthetics appear to interrupt synaptic transmission within these systems. Multiple ion channels and receptors that mediate and modulate synaptic transmission are putative targets for general anesthetics. All general anesthetics are not alike in the way they alter consciousness. For example, ketamine induces a state of... 

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