Algorithms And Data Structures

Fusion of Neural Networks, Fuzzy Sets, and Genetic by Lakhmi C. Jain, N.M. Martin PDF

By Lakhmi C. Jain, N.M. Martin

ISBN-10: 0849398045

ISBN-13: 9780849398049

Fusion of Neural Networks, Fuzzy platforms and Genetic Algorithms integrates neural networks, fuzzy structures, and evolutionary computing in method layout that permits its readers to address complexity - offsetting the demerits of 1 paradigm through the advantages of one other. This ebook offers particular tasks the place fusion suggestions were utilized. The chapters begin with the layout of a brand new fuzzy-neural controller. final chapters speak about the applying of specialist structures, neural networks, fuzzy keep watch over, and evolutionary computing options in sleek engineering structures. Fusion of Neural Networks, Fuzzy platforms and Genetic Algorithms covers the spectrum of purposes - comprehensively demonstrating some great benefits of fusion strategies in commercial purposes.

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Extra info for Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications

Example text

Finally, Figure 3d) shows the dependent input line current ia obtained using (9). The DFC is used as a case study in this chapter. Figure 4 shows a simplified converter-load system used for modeling purposes. Using the transfer function [20], and the electrical variables classifications given in Table II, the converter’s input and output voltages and currents relationships can be written as shown in (10) considering Figure 4. Figure 4 Schematic of a simplified XDFC drive, comprizing the input voltage source (Vr, Vs, Vt), input capacitive filter, the XDFC converter using ideal switches, and a three-phase delta connected load.

Software waveform reconstruction is performed by using transfer functions H and Hi, which model the converter operation. According to Table I matrix H can take 25 different forms from the 27 different electric states. These are transformed into space vectors using Park’s matrix (Table III), which are required by the XSVM used for this converter. The modeling approach chosen, based on the converter transfer function H, sets two different control objectives. The first one is to control the output line currents of the XDFC, which is the prime objective as they are the load’s currents.

Granted this, the controller will select the next converter state, which will be the one that will bring the current vector back to the reference current vector’s error zone, and do so for the longest amount of time. The actual converter state selection is realized using the XSVM. The output port control requires obtaining the input phase voltages, output line voltages, and the output line currents. To fulfill these requirements, only the input phase voltages and output line currents need to be physically measured by proper equipment, that is transformers and current sensors.

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Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications by Lakhmi C. Jain, N.M. Martin


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