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By Noah E. Friedkin and Eugene C. Johnsen

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A. (1961) Asymptotic Mdhods in the Theory 0/ No"UnelU Oscilllztio"s, New York: Gordon and Breach. Chen, S. A. (1990), Neural networks for nonlinear dynamic system modelling and identification, Internatio"al JOlUlfal 0/ Control, 56(2), 319-346. Cybenko, G. (1989) Approximation by superpositions of a sigmoidal function, MatlIematics o/Control, Signals, IUId Systems, 2, 303-314 . Funahashi, K (1989) On the approximate realization of continuous mappings by neural networks, Neural Networks, 2, 183-192.

1990), Neural networks for nonlinear dynamic system modelling and identification, Internatio"al JOlUlfal 0/ Control, 56(2), 319-346. Cybenko, G. (1989) Approximation by superpositions of a sigmoidal function, MatlIematics o/Control, Signals, IUId Systems, 2, 303-314 . Funahashi, K (1989) On the approximate realization of continuous mappings by neural networks, Neural Networks, 2, 183-192. Harber, R. and Unbehauen, H. (1990) Structure identification of nonlinear dynamic systems - a survey on input I output approaches, AU'olfUltica, 26(4), 651-677.

1960) Adaptive switching circuits, Proc. 1960 IRE WESCON Convention Record, Part 4, IRE, New York. 96-104 Chapter 2 Dynamic System Identification Using Feedforward Neural Networks A dynamic system can be described by two types of models: input-{)utput models and state-space models . This chapter describes the use of feedforward neural networks to learn to act as both types of models. 1 Input-Output Model An input-output model describes a dynamic system based on input and output data. In the discrete-time domain, an input-output model can be of the NARMAX type [Chen and Billings, 1990J or the parametric Hammerstein type [Iserman et ai, 1992].

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ATTITUDE CHANGE, AFFECT CONTROL, AND EXPECTATION STATES IN THE FORMATION OF INFLUENCE NETWORKS by Noah E. Friedkin and Eugene C. Johnsen


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