International Journal of Man-Machine Studies
Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Multilayer feedforward networks are universal approximators
Neural Networks
Fuzzy cognitive maps considering time relationships
International Journal of Human-Computer Studies
Using fuzzy cognitive maps as a system model for failure modes and effects analysis
Information Sciences: an International Journal
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Cognitive mapping and certainty neuron fuzzy cognitive maps
Information Sciences: an International Journal
Computers and Operations Research - Neural networks in business
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Stock Market Prediction with Backpropagation Networks
IEA/AIE '92 Proceedings of the 5th international conference on Industrial and engineering applications of artificial intelligence and expert systems
Modeling chaotic behavior of stock indices using intelligent paradigms
Neural, Parallel & Scientific Computations - Special issue: Advances in intelligent systems and applications
Analysis of the predictive ability of time delay neural networksapplied to the S&P 500 time series
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On causal inference in fuzzy cognitive maps
IEEE Transactions on Fuzzy Systems
Dynamical cognitive network - an extension of fuzzy cognitive map
IEEE Transactions on Fuzzy Systems
Genetic evolution of the topology and weight distribution of neural networks
IEEE Transactions on Neural Networks
Application of Fuzzy Cognitive Maps for Stock Market Modeling and Forecasting
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
A fuzzy cognitive map approach for effect-based operations: An illustrative case
Information Sciences: an International Journal
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
Benchmarking main activation functions in fuzzy cognitive maps
Expert Systems with Applications: An International Journal
Using fuzzy cognitive map for system control
WSEAS TRANSACTIONS on SYSTEMS
Partitioning study of complex system
WSEAS TRANSACTIONS on SYSTEMS
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems
Expert Systems with Applications: An International Journal
A flexible nonlinear approach to represent cause-effect relationships in FCMs
Applied Soft Computing
A dynamic fuzzy cognitive map applied to chemical process supervision
Engineering Applications of Artificial Intelligence
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Dynamic cognitive networks (DCNs) define a novel approach to functionalize cognitive mapping and complex systems analysis, which were recently supported by fuzzy cognitive maps (FCMs). The modeling and inference limitations met in FCMs, especially in situations with strong nonlinearity and temporal phenomena, pushed towards DCNs; their theoretical framework is scheduled to confront the preceding weaknesses and offer wider possibilities in causal structures management. Trying to contribute to the enhancement of DCNs, at first, systemic and environmental metaphors are introduced with practical mathematical formalisms and generalized nomenclature. Nonlinear and asymmetric cause-effect relationships, decaying mechanisms, inertial forces, diminishing effects and biases formulate a powerful set of adaptive characteristics that strengthen the operational behavior of DCNs. Second, the strategic reorientation of DCNs is attempted as generalized approximation tools. This new strategic option is verified not only in classical function approximation tests, but also in the challenging area of securities markets. The platform of evaluation of DCNs involves comparisons with a linear multiple regression model, a feed-forward neural network trained with both back-propagation and evolution strategies, a radial basis function network, and an adaptive network-based fuzzy inference system (ANFIS). Through the experiments for short-term stock price predictions, multiple issues are analyzed not only about the role of diverse DCN parameters, but also about the given problem of financial markets modeling and forecasting. Simulations distinguish DCNs as a strong methodology with noticeable adaptability in complicated patterns and broad generalization capabilities while, at the same time, the all-embracing outcomes support previous findings of partially random walk phenomena in short-term stock market forecasting attempts.