Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neurocomputing
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Input variable identification—fuzzy curves and fuzzy surfaces
Fuzzy Sets and Systems
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Neural Fuzzy Control Systems with Structure and Parameter Learning
Neural Fuzzy Control Systems with Structure and Parameter Learning
Physicists Attempt to Scale the Ivory Towers of Finance
Computing in Science and Engineering
On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach
Computational Economics
Robust Evolutionary Algorithm Design for Socio-economic Simulation
Computational Economics
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Economic modeling of financial markets attempts to model highly complex systems in which expectations can be among the dominant driving forces. It is necessary, then, to focus on how agents form expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. Agents' bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule Bases. For example if a single agent believes the exchange rate is determined by a set of possible inputs and is asked to state his relationship, his answer will probably reveal a fuzzy nature like: IF the inflation rate in the EURO-Zone is low and the GDP growth rate islarger than in the US THEN the EURO will rise against the USD.Low and larger are fuzzy terms which give a graduallinguistic meaning to crisp intervalls in the respective universes of discourse. In order to learn a Fuzzy Rule base from examples we introduce Genetic Algorithms and Artificial Neural Networks as learning operators. These examples can either be empirical data or originate from an economic simulation model. The software GENEFER (GEnetic NEural Fuzzy ExploreR) has been developedfor designing such a Fuzzy Rule Base. The design process is modular and comprises Input Identification, Fuzzification, Rule Base Generating and Rule Base Tuning. The two latter steps make use of genetic and neural learning algorithms for optimizing the Fuzzy Rule Base.