Algorithm - fuzzy

D-MS Nicotine Rat blood 2D image protein-mapping proteins algorithm fuzzy logic... 

In the input layer and hidden layer of algorithm, fuzzy inference system (FIS) takes place. To set up the fuzzy inference system, MATLAB Fuzzy Logic Toolbox is used. As shown in Fig. 3.21, three types of fuzzy inference systems are developed, namely (1) left, (2) front, and (3) right obstacle avoidance fuzzy inference systems. 

These tests generate several Gigabytes of data that are fed into a historical database. Although most of the analysis is performed automatically, human interaction is still needed to compare current and past data. Data are stored on optical CD S s from which the historical data bank are retrieved during field inspections from a mobile unit. Each of these is equipped with a CD-jukebox linked to an analysis station. The jukebox can handle 100 CD s, enough to store all previously recorded data. A dedicated software pre-fetches the historical data and compares it on-line with the newly acquired NDT-data. It is based on fuzzy algorithms applied to signal features. 

New developments which have still to be checked for their usability in data evaluation of depth profiles are artificial neural networks [2.16, 2.21-2.25], fuzzy clustering [2.26, 2.27] and genetic algorithms [2.28]. 

Sutton, R. and Marsden, G.D. (1997) A Fuzzy Autopilot Optimized using a Genetic Algorithm, Journal of Navigation, 50(1), pp. 120-131. 

Chang CL, Lo SL, Yu SL (2005) Applying fuzzy theory and genetic algorithm to interpolate precipitation. J Hydrol 314 92-104... 

Cheng CT, Ou CP, Chau KW (2002) Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration. J Hydrol 268 72-86... 

The approximate transferability of fuzzy fragment density matrices, and the associated technical, computational aspects of the idempotency constraints of assembled density matrices, as well as the conditions for adjustability and additivity of fragment density matrices are discussed in Section 4, whereas in Section 5, an algorithm for small deformations of electron densities are reviewed. The Summary in Section 6 is followed by an extensive list of relevant references. 

In the last decades not only thousands of chemical descriptors but also many advanced, powerful modeling algorithms have been made available, The older QSAR models were linear equations with one or a few parameters. Then, other tools have been introduced, such as artificial neural network, fuzzy logic, and data mining algorithms, making possible non linear models and automatic generation of mathematical solutions. 

Discusses evolutionary algorithms, cellular automata, expert systems, fuzzy logic, learning classifier systems, and evolvable developmental systems... 

Evaluation in the microprocessor may be carried out conventionally, in accordance with fuzzy logic algorithms or even on the basis of so called neural networks. To what extent such terms can be advertised to the end user as a type of quality criterion remains to be seen. For consumers these differences are largely of no consequence as they always receive clean, hygienic, problem-free laundry for which only the absolutely essential quantities of the required resources of water, energy and chemicals have been used. 

Moraga, C. Meyer zu Bexten, E. 1997. Fuzzy Knowledge-Based Genetic Algorithms. Information Sciences, Vol. 103, pp. 101-114. 

The solution for model-based clustering is based on the Expectation Maximi-zation (EM) algorithm. It uses the likelihood function and iterates between the expectation step (where the group memberships are estimated) and the maximization step (where the parameters are estimated). As a result, each object receives a membership to each cluster like in fuzzy clustering. The overall cluster result can be evaluated by the value of the negative likelihood function which should be as small as possible. This allows judging which model for the clusters is best suited (spherical clusters, elliptical clusters) and which number of clusters, k, is most appropriate. 

FIGURE 6.18 Cluster validity V(k), see Equation 6.13, for the algorithms fc-means, fuzzy c-means, and model-based clustering with varying number of clusters. The left picture is the result for the example used in Figure 6.8 (three spherical clusters), the right picture results from the analysis of the data from Figure 6.9 (two elliptical clusters and one spherical cluster). 

Bezdek, J. C. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, 1981. 

The validity discriminant discussed in this section is the descendant of an earlier cluster validity measure used by Gunderson ( ) to assess the quality of cluster configurations obtained in an application of the Fuzzy ISODATA algorithms. It is closely related to a method suggested by Sneath ( ) for testing the distinctness, i.e. separation, of two clusters, and also borrows from the ideas of Fisher s linear discriminant theory (see chapt. 4, Duda and Hart,(2 0). The validity discriminant attempts to measure the separation between the classes of a cluster configuration usually, but not necessarily, obtained by application of the FCV algorithms. A brief description follows ... 

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