A biological brain-inspired fuzzy neural network: Fuzzy emotional neural network

Abstract In this paper, a brain-inspired fuzzy emotional neural network (FUZZ-ENN) is proposed for uncertainty prediction tasks in real world applications. In the proposed FUZZ-ENN, amygdala connections are modeled by fuzzy IF-THEN behavioral rules and orbitofrontal module inhibits the amygdala responses in order to decrease the uncertainty. This computational model is based on the inhibitory connections in the human emotional brain’s nervous system inhibiting the uncertainty. In this paper, genetic algorithm is applied for optimal tuning of crisp numerical and fuzzy parameters of the proposed model. A traditional neural model and a two layered emotional neural network (ENN) are also implemented for comparison purposes on the electrical load and wind power forecasting problem and the prediction of geomagnetic activity indices as two real world case studies. Numerical results indicate the superiority of the proposed approach in term of lower uncertainty in the prediction.

[1]  Konrad Kording,et al.  Annals of the New York Academy of Sciences Bayesian Models: the Structure of the World, Uncertainty, Behavior, and the Brain , 2022 .

[2]  Babak Nadjar Araabi,et al.  Introducing adaptive neurofuzzy modeling with online learning method for prediction of time-varying solar and geomagnetic activity indices , 2010, Expert Syst. Appl..

[3]  Haizhou Li,et al.  Rapid Feedforward Computation by Temporal Encoding and Learning With Spiking Neurons , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Caro Lucas,et al.  Learning based brain emotional intelligence as a new aspect for development of an alarm system , 2008, Soft Comput..

[5]  Georgios Balasis,et al.  Investigating dynamical complexity in the magnetosphere using various entropy measures , 2009 .

[6]  Ehsan Lotfi,et al.  Adaptive brain emotional decayed learning for online prediction of geomagnetic activity indices , 2014, Neurocomputing.

[7]  Abdollah Kavousi-Fard,et al.  A hybrid method based on wavelet, ANN and ARIMA model for short-term load forecasting , 2014, J. Exp. Theor. Artif. Intell..

[8]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[9]  Frederick J. Rich,et al.  Solar wind structure sources and periodicities of auroral electron power over three solar cycles , 2009 .

[10]  Jianxue Wang,et al.  Review on probabilistic forecasting of wind power generation , 2014 .

[11]  Paul J. Whalen,et al.  The uncertainty of it all , 2007, Trends in Cognitive Sciences.

[12]  Dan Zhang,et al.  Mixed H∞ and passivity based state estimation for fuzzy neural networks with Markovian-type estimator gain change , 2014, Neurocomputing.

[13]  P. McSharry,et al.  Short-Term Load Forecasting Methods: An Evaluation Based on European Data , 2007, IEEE Transactions on Power Systems.

[14]  Goran Pejanović,et al.  Improved wind forecasts for wind power generation using the Eta model and MOS (Model Output Statistics) method , 2014 .

[15]  Chia-Feng Juang,et al.  Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm , 2008, Fuzzy Sets Syst..

[16]  Qiang Shen,et al.  Fuzzy-rough data reduction with ant colony optimization , 2005, Fuzzy Sets Syst..

[17]  V M F Mendes,et al.  Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Wind Power Forecasting in Portugal , 2011, IEEE Transactions on Sustainable Energy.

[18]  Sung-Kwun Oh,et al.  Polynomial-based radial basis function neural networks (P-RBF NNs) realized with the aid of particle swarm optimization , 2011, Fuzzy Sets Syst..

[19]  Ehsan Lotfi,et al.  Gene expression microarray classification using PCA-BEL , 2014, Comput. Biol. Medicine.

[20]  Qinke Peng,et al.  Detecting Susceptibility to Breast Cancer with SNP-SNP Interaction Using BPSOHS and Emotional Neural Networks , 2016, BioMed research international.

[21]  Ehsan Lotfi,et al.  BRAIN EMOTIONAL LEARNING-BASED PATTERN RECOGNIZER , 2013, Cybern. Syst..

[22]  Ruddy Blonbou,et al.  Very short-term wind power forecasting with neural networks and adaptive Bayesian learning , 2011 .

[23]  Ali Azadeh,et al.  An emotional learning-neuro-fuzzy inference approach for optimum training and forecasting of gas consumption estimation models with cognitive data , 2015 .

[24]  Christian Büchel,et al.  Oxytocin Attenuates Amygdala Responses to Emotional Faces Regardless of Valence , 2007, Biological Psychiatry.

[25]  Ergin Erdem,et al.  ARMA based approaches for forecasting the tuple of wind speed and direction , 2011 .

[26]  P. Petta,et al.  Computational models of emotion , 2010 .

[27]  Abbas Khosravi,et al.  Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[28]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[29]  Giacomo Capizzi,et al.  Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Oscar Castillo,et al.  A new approach for time series prediction using ensembles of ANFIS models , 2012, Expert Syst. Appl..

[31]  Joseph E LeDoux.  Emotion Circuits in the Brain , 2000 .

[32]  Ehsan Lotfi,et al.  Competitive Brain Emotional Learning , 2018, Neural Processing Letters.

[33]  Kit Po Wong,et al.  Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine , 2014, IEEE Transactions on Power Systems.

[34]  Babak Nadjar Araabi,et al.  Implementation of Emotional Controller for Interior Permanent Magnet Synchronous Motor Drive , 2006, Conference Record of the 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting.

[35]  Chia-Feng Juang,et al.  Hierarchical Singleton-Type Recurrent Neural Fuzzy Networks for Noisy Speech Recognition , 2007, IEEE Transactions on Neural Networks.

[36]  Ekaterina Mangalova,et al.  Wind power forecasting using the k-nearest neighbors algorithm , 2014 .

[37]  Ehsan Lotfi,et al.  A Neural Basis Computational Model of Emotional Brain for Online Visual Object Recognition , 2014, Appl. Artif. Intell..

[38]  G. McCarthy,et al.  Decisions under Uncertainty: Probabilistic Context Influences Activation of Prefrontal and Parietal Cortices , 2005, The Journal of Neuroscience.

[39]  S. Setayeshi,et al.  An Improved Model Of Brain Emotional Learning Algorithm Based On Interval Knowledge , 2015 .

[40]  Adnan Khashman,et al.  An emotional system with application to blood cell type identification , 2012 .

[41]  Vera Chung,et al.  Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization , 2014 .

[42]  M. Negnevitsky,et al.  Very short-term wind forecasting for Tasmanian power generation , 2006, 2006 IEEE Power Engineering Society General Meeting.

[43]  Yingnan Pan,et al.  New mean square exponential stability condition of stochastic fuzzy neural networks , 2015, Neurocomputing.

[44]  Paras Mandal,et al.  A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting , 2014, IEEE Transactions on Power Systems.

[45]  Meng Joo Er,et al.  NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches , 2005, Fuzzy Sets Syst..

[46]  T. Dalgleish.  The emotional brain , 2004, Nature Reviews Neuroscience.

[47]  Stefano Alessandrini,et al.  A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data , 2013 .

[48]  Adnan Khashman,et al.  Modeling cognitive and emotional processes: A novel neural network architecture , 2010, Neural Networks.

[49]  Huaguang Zhang,et al.  Novel stability criterions of a new fuzzy cellular neural networks with time-varying delays , 2009, Neurocomputing.

[50]  Roelof K. Brouwer,et al.  Modified fuzzy c-means for ordinal valued attributes with particle swarm for optimization , 2010, Fuzzy Sets Syst..

[51]  Abdollah Kavousi-Fard,et al.  A new hybrid correction method for short-term load forecasting based on ARIMA, SVR and CSA , 2013, J. Exp. Theor. Artif. Intell..

[52]  Ehsan Lotfi,et al.  Practical emotional neural networks , 2014, Neural Networks.

[53]  Abdollah Kavousi-Fard.  A new fuzzy-based feature selection and hybrid TLA–ANN modelling for short-term load forecasting , 2013, J. Exp. Theor. Artif. Intell..

[54]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[55]  Caro Lucas,et al.  Introducing Belbic: Brain Emotional Learning Based Intelligent Controller , 2004, Intell. Autom. Soft Comput..

[56]  Witold Pedrycz,et al.  Granular Neural Networks: Concepts and Development Schemes , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[57]  Adnan Khashman,et al.  Credit risk evaluation using neural networks: Emotional versus conventional models , 2011, Appl. Soft Comput..

[58]  Gin-Der Wu,et al.  An enhanced discriminability recurrent fuzzy neural network for temporal classification problems , 2014, Fuzzy Sets Syst..

[59]  David C. Yu,et al.  An Economic Dispatch Model Incorporating Wind Power , 2008, IEEE Transactions on Energy Conversion.

[60]  Saeid Nahavandi,et al.  Prediction Interval Construction and Optimization for Adaptive Neurofuzzy Inference Systems , 2011, IEEE Transactions on Fuzzy Systems.

[61]  Ramkumar Bala,et al.  Real‐time prediction of magnetospheric activity using the Boyle Index , 2009 .

[62]  Farshid Keynia,et al.  Short-term wind power forecasting using ridgelet neural network , 2011 .

[63]  Adnan Khashman,et al.  Blood Cell Identification using Emotional Neural Networks , 2009, J. Inf. Sci. Eng..

[64]  Adnan Khashman,et al.  Emotional System for Military Target Identification , 2009 .

[65]  J. Fedder,et al.  Modeling seasonal variations of auroral particle precipitation in a global-scale magnetosphere-ionosphere simulation , 2009 .

[66]  P. Thagard,et al.  Hot Thought: Mechanisms and Applications of Emotional Cognition , 2006 .

[67]  Mohammad Teshnehlab,et al.  Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter , 2009, Fuzzy Sets Syst..

[68]  Jerry M. Mendel,et al.  Interval Type-2 Fuzzy Logic Systems Made Simple , 2006, IEEE Transactions on Fuzzy Systems.

[69]  J.B. Theocharis,et al.  Long-term wind speed and power forecasting using local recurrent neural network models , 2006, IEEE Transactions on Energy Conversion.

[70]  Joseph E LeDoux.  The emotional brain , 1996 .

[71]  S. A. Billings,et al.  Forecasting the geomagnetic activity of the Dst index using multiscale radial basis function networks , 2007 .

[72]  H. Critchley,et al.  A common role of insula in feelings, empathy and uncertainty , 2009, Trends in Cognitive Sciences.

[73]  Peng Kou,et al.  A sparse heteroscedastic model for the probabilistic load forecasting in energy-intensive enterprises , 2014 .

[74]  Adnan Khashman,et al.  A Modified Backpropagation Learning Algorithm With Added Emotional Coefficients , 2008, IEEE Transactions on Neural Networks.

[75]  Yang-Yin Lin,et al.  A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing , 2010, Fuzzy Sets Syst..

[76]  J. Rosen,et al.  Animal studies of amygdala function in fear and uncertainty: Relevance to human research , 2006, Biological Psychology.

[77]  Zhang Zhisheng,et al.  Chaotic electromagnetism-like mechanism algorithm for economic load dispatch of power system , 2013 .

[78]  Marcos Angel Gonzalez-Olvera,et al.  A new recurrent neurofuzzy network for identification of dynamic systems , 2007, Fuzzy Sets Syst..

[79]  Saeid Nahavandi,et al.  Prediction Intervals to Account for Uncertainties in Travel Time Prediction , 2011, IEEE Transactions on Intelligent Transportation Systems.