Practical Issues in Temporal Difference Learning
Machine Learning
Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
Genetic programming for model selection of TSK-fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Applying brain emotional learning algorithm for multivariable control of HVAC systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
A Dish Parallel BP for Traffic Flow Forecasting
CIS '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
A Short-Term Prediction Model for Forecasting Traffic Information Using Bayesian Network
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 01
CIS '08 Proceedings of the 2008 International Conference on Computational Intelligence and Security - Volume 02
Study of Traffic Flow Prediction Model at Intersection Based on R-FNN
ISBIM '08 Proceedings of the 2008 International Seminar on Business and Information Management - Volume 01
Short-Term Traffic Flow Prediction Based on Parallel Quasi-Newton Neural Network
ICMTMA '09 Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03
A neuro-fuzzy approach for prediction of human work efficiency in noisy environment
Applied Soft Computing
Some studies in machine learning using the game of checkers
IBM Journal of Research and Development
Road Traffic Flow Prediction with a Time-Oriented ARIMA Model
NCM '09 Proceedings of the 2009 Fifth International Joint Conference on INC, IMS and IDC
The Application of the Locally Linear Model Tree on Customer Churn Prediction
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
A survey of fuzzy clustering algorithms for pattern recognition. I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A survey of fuzzy clustering algorithms for pattern recognition. II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New membership functions for effective design and implementation of fuzzy systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning
IEEE Transactions on Fuzzy Systems
Asymptotic statistical theory of overtraining and cross-validation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Prediction of noisy chaotic time series using an optimal radial basis function neural network
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Backward Q-learning: The combination of Sarsa algorithm and Q-learning
Engineering Applications of Artificial Intelligence
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Bounded rationally idea, rather that optimization idea, have result and better performance in decision making theory. Bounded rationality is the idea in decision making, rationality of individuals is limited by the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make decisions. The emotional theory is an important topic presented in this field. The new methods in the direction of purposeful forecasting issues, which are based on cognitive limitations, are presented in this study. The presented algorithms in this study are emphasizes to rectify the learning the peak points, to increase the forecasting accuracy, to decrease the computational time and comply the multi-object forecasting in the algorithms. The structure of the proposed algorithms is based on approximation of its current estimate according to previously learned estimates. The short term traffic flow forecasting is a real benchmark that has been studied in this area. Traffic flow is a good measure of traffic activity. The time-series data used for fitting the proposed models are obtained from a two lane street I-494 in Minnesota City, USA. The research discuss the strong points of new method based on neurofuzzy and limbic system structure such as Locally Linear Neurofuzzy network (LLNF) and Brain Emotional Learning Based Intelligent Controller (BELBIC) models against classical and other intelligent methods such as Radial Basis Function (RBF), Takagi-Sugeno (T-S) neurofuzzy, and Multi-Layer Perceptron (MLP), and the effect of noise on the performance of the models is also considered. Finally, findings confirmed the significance of structural brain modeling beyond the classical artificial neural networks.