Kosko, B., Fuzzy Thinking, Hyperion, 1993 Home/Search Document Not in Database Summary Related Articles Check |
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Application of Neuro-Fuzzy Systems to Behavioral.. - Gary George Raytheon (1999) (2 citations) (Correct)
....in the solution space which result in creation of the fuzzy sets. The ANN learns these clusters based on actual human behavior test data. A further advantage is that the solution space rather than being represented point by point as some expert systems clumps the space as described by Kosko [2,3]. This results in fewer rules and lower computer resources. The experiment to build and validate the model includes a compensatory task performed by several human subjects to develop a training and test set of data in this human behavior. In the compensatory tracking task the subject attempts to ....
....sets of fuzzy rules defined by these fuzzy shapes. The function uses R rules (seven in this case) to determine an M element output vector (one here for modeled human control) given an N element input vector X (one for E(s) The R rules are defined with an antecedent matrix A( this case A is [1 2 3 4 5 6 7] ) and a consequence matrix C (in this case C is [1 2 3 4 5 6 7] which correspond to the seven fuzzy rules listed above. Each ith row of the antecedent matrix A indicates the conditions for which that rule applies. Each such row contains N values where the jth value indicates which fuzzy set of ....
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) Kosko, Bart, Fuzzy Thinking, Hyperion, N. Y., N. Y., 1992.
Classification Problems: an old question with new solutions? - Costa (Correct)
....robust in the presence of noise but have poor explanatory capabilities. 3.5 Fuzzy Logic Fuzzy Logic was introduced by L. Zadeh and is, in some sense, a consequence of the way humans communicate and reason. In Koskos well known example, if we are eating an apple when does it stop being an apple (Kosko 1993) The real world is fuzzy and Fuzzy Systems have the ability to deal with uncertainty. Knowledge in FS is coded into membership functions of fuzzy sets whose labels are the values of a linguistic variable. Input Output hidden w i, j = w i, j j x i , j Classification Problems: an old question ....
B. Kosko, Fuzzy Thinking, Flamingo, 1993.
SUMPY: A Fuzzy Software Agent - Song, Franklin, Negatu (1996) (2 citations) (Correct)
....perceive information from their environment and produce next action, so changing the environment. Though physical robots and softbots differ greatly in their implementation, they may share their control architecture, a subsumption architecture for one example. Fuzziness abounds in the real world [9]. Fuzzy knowledge and fuzzy 2 reasoning are useful when making decisions with fuzzy information. Software agents can face the same problem of fuzziness. Hence, fuzzy processing could be a part of their control structure Here we describe our research based on a software agent, named SUMPY, built ....
....channels. Such structures also have the advantages of robustness and extensibility. 2. 2 Fuzzy controllers Fuzzy controllers are capable of utilizing knowledge elicited from human operators to solve control problems for which precise mathematical models are difficult or even impossible to construct[8,9]. Such a fuzzy controller employs a knowledge base, expressed in terms of relevant fuzzy inference rules, and an appropriate inference engine to solve a given control problem. A general fuzzy controller (Fig. 2.2) consists of four modules: a fuzzy rule base, a fuzzy inference engine, a ....
Kosko, Bart (1993), Fuzzy Thinking, Hyperion Press, New York.
Rule Based Fuzzy Cognitive Maps: - Fuzzy Causal Relations (Correct)
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Kosko, B., Fuzzy Thinking, Hyperion, 1993
Automatic Implementation and Simulation of Dynamic Qualitative - Systems Using Fuzzy (Correct)
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Kosko,B., "Fuzzy Thinking", Hyperion, 1993
Rule Based Fuzzy Cognitive Maps - Expressing Time in - Qualitative System Dynamics (Correct)
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Kosko,B., "Fuzzy Thinking", Hyperion, 1993
Rule Based Fuzzy Cognitive Maps and Fuzzy Cognitive Maps -- - Comparative Study Joo (Correct)
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Kosko, B., Fuzzy Thinking, Hyperion, 1993
Humanist Computing for Knowledge Discovery from Ordered Datasets - Rossiter (Correct)
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B. Kosko. Fuzzy Thinking. Flamingo, 1993.
Soft Modular Autonomous Intelligent Cruise Control (AICC) System - Shaout, Jarrah (2000) (Correct)
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Kosko, B., 1993," Fuzzy Thinking", Hyperion, New York.
Humanist Computing for Knowledge Discovery from Ordered Datasets - Rossiter (2000) (Correct)
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B. Kosko. Fuzzy Thinking. Flamingo, 1993.
Adaptive Fuzzy Cognitive Maps for Hyperknowledge.. - Carlsson, Fullér (1996) (2 citations) (Correct)
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B. Kosko, Fuzzy Thinking, Hyperion/Disney Books, 1993.
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