Download Computational Intelligence: A Methodological Introduction by Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian PDF
By Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, Pascal Held
This textbook offers a transparent and logical creation to the sector, protecting the basic ideas, algorithms and sensible implementations in the back of efforts to increase structures that convey clever habit in complicated environments. This more advantageous moment version has been totally revised and extended with new content material on swarm intelligence, deep studying, fuzzy info research, and discrete determination graphs. good points: presents supplementary fabric at an linked site; includes various classroom-tested examples and definitions in the course of the textual content; provides priceless insights into all that's invaluable for the winning program of computational intelligence equipment; explains the theoretical heritage underpinning proposed strategies to universal difficulties; discusses in nice aspect the classical parts of synthetic neural networks, fuzzy structures and evolutionary algorithms; reports the most recent advancements within the box, overlaying such themes as ant colony optimization and probabilistic graphical models.
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Additional resources for Computational Intelligence: A Methodological Introduction
E. L. McClelland (MIT Press, Cambridge, 1986a), pp. E. E. J. Williams, Learning representations by back-propagating errors. D. J. Werbos, Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. D. O. Widner, Single State Logic. in AIEE Fall General Meeting (1960). Reprinted in (Wasserman 1989) B. E. Hoff, Adaptive switching circuits, in IRE WESCON Convention Record (Institute of Radio Engineers, New York, 1960), pp. 96–104 A. Zell, Simulation Neuronaler Netze (Addison-Wesley, Stuttgart, 1996) 4 General Neural Networks In this chapter we introduce a general model of (artificial) neural networks that captures (more or less) all special forms, which we consider in the following chapters.
14. Again the left diagram shows the error for the input x = 0, the middle diagram the error for the input x = 1 and the right diagram the sum of these individual errors. If a threshold logic unit now produces a wrong output, we adapt the weight and the threshold in such a way that the error is reduced. That is, we try to “descent in the error landscape”. With the modified error function this is possible, because we can read from it “locally” (that is, without a visual inspection of the whole error function, but merely by looking at the shape of the error function at the point that 2 The somewhat imprecise notion “almost all points” can be made mathematically precise by drawing on measure theory: the set of points at which the error function changes has measure 0.
This computation can be influenced by additional parameters σ1 , . . , σl (see, for instance, Sect. 5). From the network input, a certain number of parameters θ1 , . . , θk , and possibly a feedback of the current activation (u) of the neuron u (see, for instance, Chap. 9) the activation function f act computes the (u) new activation actu of the neuron u. Finally the output function f out computes the output of the neuron u from its activation. The external input extu sets the (initial) activation of the neuron u, if it is an input neuron (see below).