A hybrid cascade neural network with an optimized pool in each cascade
暂无分享,去创建一个
[1] Plamen P. Angelov,et al. Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..
[2] Edwin Lughofer,et al. Improving the robustness of data-driven fuzzy systems with regularization , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).
[3] E. Mizutani,et al. Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.
[4] E. Lughofer,et al. Model-based fault detection in multi-sensor measurement systems , 2004, 2004 2nd International IEEE Conference on 'Intelligent Systems'. Proceedings (IEEE Cat. No.04EX791).
[5] Edwin Lughofer,et al. Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications , 2011, Studies in Fuzziness and Soft Computing.
[6] Plamen P. Angelov,et al. Data-driven evolving fuzzy systems using eTS and FLEXFIS: comparative analysis , 2008, Int. J. Gen. Syst..
[7] S. Kaczmarz. Approximate solution of systems of linear equations , 1993 .
[8] Ulrich Bodenhofer,et al. Incremental Learning of Fuzzy Basis Function Networks with a Modified Version of Vector Quantization , 2006 .
[9] Edwin Lughofer,et al. On-Line Fault Detection with Data-Driven Evolving Fuzzy Models , 2008, Control. Intell. Syst..
[10] Visakan Kadirkamanathan,et al. A Function Estimation Approach to Sequential Learning with Neural Networks , 1993, Neural Computation.
[11] Plamen Angelov,et al. On-line Identification of MIMO Evolving Takagi- , 2004 .
[12] Plamen P. Angelov,et al. Evolving Fuzzy-Rule-Based Classifiers From Data Streams , 2008, IEEE Transactions on Fuzzy Systems.
[13] Edwin Lughofer,et al. Applying evolving fuzzy models with adaptive local error bars to on-line fault detection , 2008, 2008 3rd International Workshop on Genetic and Evolving Systems.
[14] Edwin Lughofer,et al. An approach to model-based fault detection in industrial measurement systems with application to engine test benches , 2006 .
[15] Nikola K. Kasabov,et al. Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning , 2001, IEEE Trans. Syst. Man Cybern. Part B.
[16] Edwin Lughofer,et al. SparseFIS: Data-Driven Learning of Fuzzy Systems With Sparsity Constraints , 2010, IEEE Transactions on Fuzzy Systems.
[17] Y Lu,et al. A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks , 1997, Neural Computation.
[18] Christian Lebiere,et al. The Cascade-Correlation Learning Architecture , 1989, NIPS.
[19] Edwin Lughofer,et al. Extensions of vector quantization for incremental clustering , 2008, Pattern Recognit..
[20] Yevgeniy Bodyanskiy,et al. HYBRID CASCADE NEURAL NETWORK BASED ON WAVELET-NEURON , 2011 .
[21] E. Lughofer. Process Safety Enhancements for Data-Driven Evolving Fuzzy Models , 2006, 2006 International Symposium on Evolving Fuzzy Systems.
[22] D.P. Filev,et al. An approach to online identification of Takagi-Sugeno fuzzy models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[23] Illya Kokshenev,et al. An adaptive learning algorithm for a neo fuzzy neuron , 2003, EUSFLAT Conf..
[24] Paramasivan Saratchandran,et al. Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction , 2006, Fuzzy Sets Syst..
[25] Frank Fallside,et al. An adaptive training algorithm for back propagation networks , 1987 .
[26] Edwin Lughofer,et al. On-line evolving image classifiers and their application to surface inspection , 2010, Image Vis. Comput..
[27] Nikola Kasabov,et al. Evolving connectionist systems , 2002 .
[28] E. Lughofer,et al. Evolving fuzzy classifiers using different model architectures , 2008, Fuzzy Sets Syst..
[29] Yevgeniy V. Bodyanskiy,et al. A new learning algorithm for a forecasting neuro-fuzzy network , 2003, Integr. Comput. Aided Eng..
[30] Javier R. Movellan,et al. Benefits of gain: speeded learning and minimal hidden layers in back-propagation networks , 1991, IEEE Trans. Syst. Man Cybern..
[31] Nikola K. Kasabov,et al. DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..
[32] Edwin Lughofer,et al. On Human-Machine Interaction during Online Image Classifier Training , 2008, 2008 International Conference on Computational Intelligence for Modelling Control & Automation.
[33] Yevgeniy Bodyanskiy,et al. EVOLVING CASCADE NEURAL NETWORK BASED ON MULTIDIMESNIONAL EPANECHNIKOV'S KERNELS AND ITS LEARNING ALGORITHM , 2011 .
[34] Yevgeniy V. Bodyanskiy,et al. An Adaptive Learning Algorithm for a Neuro-fuzzy Network , 2001, Fuzzy Days.
[35] Robert J. Schalkoff,et al. Artificial neural networks , 1997 .
[36] Bernard Widrow,et al. Adaptive switching circuits , 1988 .
[37] Plamen P. Angelov,et al. Evolving Single- And Multi-Model Fuzzy Classifiers with FLEXFIS-Class , 2007, 2007 IEEE International Fuzzy Systems Conference.
[38] Yevgeniy Bodyanskiy,et al. THE CASCADE GROWING NEURAL NETWORK USING QUADRATIC NEURONS AND ITS LEARNING ALGORITHMS FOR ON-LINE INFORMATION PROCESSING , 2009 .
[39] Plamen P. Angelov,et al. Architectures for evolving fuzzy rule-based classifiers , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.
[40] N. Kasabov,et al. Incremental learning in autonomous systems: evolving connectionist systems for on-line image and speech recognition , 2005, IEEE Workshop on Advanced Robotics and its Social Impacts, 2005..
[41] G. V. Barkan,et al. CASCADE NEURAL NETWORKS , 1999 .
[43] Edwin Lughofer,et al. On Dynamic Selection of the Most Informative Samples in Classification Problems , 2010, 2010 Ninth International Conference on Machine Learning and Applications.
[44] P. Angelov,et al. Evolving Fuzzy Systems from Data Streams in Real-Time , 2006, 2006 International Symposium on Evolving Fuzzy Systems.
[45] Plamen P. Angelov,et al. Handling drifts and shifts in on-line data streams with evolving fuzzy systems , 2011, Appl. Soft Comput..
[46] Luís B. Almeida,et al. Speeding up Backpropagation , 1990 .
[47] Edwin Lughofer,et al. Human–Machine Interaction Issues in Quality Control Based on Online Image Classification , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[48] P. Angelov,et al. Two approaches to data-driven design of evolving fuzzy systems: eTS and FLEXFIS , 2005, NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society.
[49] Plamen P. Angelov,et al. Detecting and Reacting on Drifts and Shifts in On-Line Data Streams with Evolving Fuzzy Systems , 2009, IFSA/EUSFLAT Conf..
[50] Lutz Prechelt,et al. Investigation of the CasCor Family of Learning Algorithms , 1997, Neural Networks.
[51] Takeshi Yamakawa,et al. Soft Computing Based Signal Prediction, Restoration, and Filtering , 1997 .
[52] TSUTOMU MIKI. Analog Implementation of Neo-Fuzzy Neuron and Its On-board Learning , 1999 .
[53] Edwin Lughofer,et al. Evolving Vector Quantization for Classification of On-Line Data Streams , 2008, 2008 International Conference on Computational Intelligence for Modelling Control & Automation.
[54] Edwin Lughofer,et al. FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models , 2008, IEEE Transactions on Fuzzy Systems.
[55] Nikola Kasabov,et al. Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines , 2002, IEEE Transactions on Neural Networks.
[56] Kumpati S. Narendra,et al. Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.
[57] E. Lughofer,et al. Filtering of dynamic measurements in intelligent sensors for fault detection based on data-driven models , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).
[58] Yevgeniy Bodyanskiy,et al. THE CASCADE ORTHOGONAL NEURAL NETWORK , 2008 .
[59] Lennart Ljung,et al. System Identification: Theory for the User , 1987 .
[60] Bogdan Raducanu,et al. Online pattern recognition and machine learning techniques for computer-vision: Theory and applications , 2010, Image Vis. Comput..
[61] Nikola Kasabov,et al. Evolving computational intelligence systems , 2005 .
[62] Edwin Lughofer,et al. Premise parameter estimation and adaptation in fuzzy systems with open-loop clustering methods , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).
[63] Nikola Kasabov,et al. Evolving Connectionist Systems: The Knowledge Engineering Approach , 2007 .
[64] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[65] Eyke Hüllermeier,et al. Improving the interpretability of data-driven evolving fuzzy systems , 2005, EUSFLAT Conf..
[66] Plamen P. Angelov,et al. On-line identification of MIMO evolving Takagi- Sugeno fuzzy models , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).
[67] Plamen Angelov,et al. Evolving Intelligent Systems: Methodology and Applications , 2010 .
[68] Geoff Holmes,et al. A Modified Quickprop Algorithm , 1991, Neural Computation.