Variable-structure-systems based approach for online learning of spiking neural networks and its experimental evaluation

Abstract Raising the level of biological realism by utilizing the timing of individual spikes, spiking neural networks (SNNs) are considered to be the third generation of artificial neural networks. In this work, a novel variable-structure-systems based approach for online learning of SNN is developed and tested on the identification and speed control of a real-time servo system. In this approach, neurocontroller parameters are used to define a time-varying sliding surface to lead the control error signal to zero. To prove the convergence property of the developed algorithm, the Lyapunov stability method is utilized. The results of the real-time experiments on the laboratory servo system for a number of different load conditions including nonlinear and time-varying ones indicate that the control structure exhibits a highly robust behavior against disturbances and sudden changes in the command signal.

[1]  Zhihong Man,et al.  A fuzzy neural network approximator with fast terminal sliding mode and its applications , 2004, Fuzzy Sets Syst..

[2]  Vadim I. Utkin,et al.  Sliding mode control in electromechanical systems , 1999 .

[3]  Hojjat Adeli,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007, Integr. Comput. Aided Eng..

[4]  Jianguo Xin,et al.  Supervised learning with spiking neural networks , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[5]  Okyay Kaynak,et al.  A Dynamic Method to Forecast the Wheel Slip for Antilock Braking System and Its Experimental Evaluation , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  S. Thorpe,et al.  Spike times make sense , 2005, Trends in Neurosciences.

[7]  Okyay Kaynak,et al.  Neural network modeling and control of cement mills using a variable structure systems theory based on-line learning mechanism , 2004 .

[8]  Sander M. Bohte,et al.  Error-backpropagation in temporally encoded networks of spiking neurons , 2000, Neurocomputing.

[9]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[10]  Kevin Stanley McFall,et al.  Automated design parameter selection for neural networks solving coupled partial differential equations with discontinuities , 2013, J. Frankl. Inst..

[11]  Okyay Kaynak,et al.  Online learning in adaptive neurocontrol schemes with a sliding mode algorithm , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[12]  Okyay Kaynak,et al.  Sliding Mode Control Approach for Online Learning as Applied to Type-2 Fuzzy Neural Networks and Its Experimental Evaluation , 2012, IEEE Transactions on Industrial Electronics.

[13]  Andrew Adamatzky,et al.  Evolution of Plastic Learning in Spiking Networks via Memristive Connections , 2012, IEEE Transactions on Evolutionary Computation.

[14]  Okyay Kaynak,et al.  A type-2 fuzzy wavelet neural network for system identification and control , 2013, J. Frankl. Inst..

[15]  Nikola K. Kasabov,et al.  Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition , 2012, WCCI.

[16]  Chia-Nan Ko.  WSVR-based fuzzy neural network with annealing robust algorithm for system identification , 2012, J. Frankl. Inst..

[17]  Andon V. Topalov,et al.  Fast genetic on-line learning algorithm for neural network and its application to temperature control , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[18]  Wolfgang Maass,et al.  Fast Sigmoidal Networks via Spiking Neurons , 1997, Neural Computation.

[19]  Murray Shanahan,et al.  Training a spiking neural network to control a 4-DoF robotic arm based on Spike Timing-Dependent Plasticity , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[20]  Lukasz A. Kurgan,et al.  Recognition of Partially Occluded and Rotated Images With a Network of Spiking Neurons , 2010, IEEE Transactions on Neural Networks.

[21]  Romain Brette,et al.  Brian Hears: Online Auditory Processing Using Vectorization Over Channels , 2011, Front. Neuroinform..

[22]  Nasser Sadati,et al.  Adaptive multi-model sliding mode control of robotic manipulators using soft computing , 2008, Neurocomputing.

[23]  Hani Hagras,et al.  Evolving spiking neural network controllers for autonomous robots , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[24]  Po-Chen Chen,et al.  RETRACTED: Modified intelligent genetic algorithm-based adaptive neural network control for uncertain structural systems , 2013 .

[25]  G. Laurent,et al.  Odour encoding by temporal sequences of firing in oscillating neural assemblies , 1996, Nature.

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

[27]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[28]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[29]  Ruey-Jing Lian.  Design of an enhanced adaptive self-organizing fuzzy sliding-mode controller for robotic systems , 2012, Expert Syst. Appl..

[30]  Linda Bushnell,et al.  Fast Modifications of the SpikeProp Algorithm , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[31]  Zhigang Zeng,et al.  Deformation Prediction of Landslide Based on Improved Back-propagation Neural Network , 2012, Cognitive Computation.

[32]  Ammar Belatreche,et al.  Advances in Design and Application of Spiking Neural Networks , 2006, Soft Comput..

[33]  Mohammad-R. Akbarzadeh-T,et al.  Multi-objective meta level soft computing-based evolutionary structural design , 2007, J. Frankl. Inst..

[34]  Okyay Kaynak,et al.  The fusion of computationally intelligent methodologies and sliding-mode control-a survey , 2001, IEEE Trans. Ind. Electron..

[35]  Andrzej Kasiński,et al.  Comparison of supervised learning methods for spike time coding in spiking neural networks , 2006 .

[36]  Okyay Kaynak,et al.  Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks , 2012, Evolving Systems.

[37]  B. Chandra,et al.  Classification of gene expression data using Spiking Wavelet Radial Basis Neural Network , 2014, Expert Syst. Appl..

[38]  J. David Schaffer,et al.  Evolving spiking neural networks for robot control , 2011, Complex Adaptive Systems.

[39]  R. Johansson,et al.  First spikes in ensembles of human tactile afferents code complex spatial fingertip events , 2004, Nature Neuroscience.

[40]  D Gamez,et al.  iSpike: a spiking neural interface for the iCub robot , 2012, Bioinspiration & biomimetics.

[41]  Hojjat Adeli,et al.  Spiking Neural Networks , 2009, Int. J. Neural Syst..

[42]  Da Lin,et al.  Observer-based decentralized fuzzy neural sliding mode control for interconnected unknown chaotic systems via network structure adaptation , 2010, Fuzzy Sets Syst..

[43]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[44]  O. Kaynak,et al.  Spiking neural networks for identification and control of dynamic plants , 2012, 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).

[45]  Mikko Kolehmainen,et al.  Evolving the neural network model for forecasting air pollution time series , 2004, Eng. Appl. Artif. Intell..

[46]  Faa-Jeng Lin,et al.  DSP-based synchronous control of dual linear motors via sugeno type fuzzy neural network compensator , 2011, 2011 International Conference on Electrical Machines and Systems.

[47]  Xinghuo Yu,et al.  Sliding-Mode Control With Soft Computing: A Survey , 2009, IEEE Transactions on Industrial Electronics.

[48]  Wulfram Gerstner,et al.  SPIKING NEURON MODELS Single Neurons , Populations , Plasticity , 2002 .

[49]  Ioana Sporea,et al.  Supervised Learning in Multilayer Spiking Neural Networks , 2012, Neural Computation.

[50]  Antônio de Pádua Braga,et al.  Sliding mode algorithm for training multilayer artificial neural networks , 1998 .