Local model networks and local learning

The Local Model Networks (networks composed of locally accurate models, where the output is interpolated by smooth locally active basis functions) described in this paper provide a solid basis for practical modelling tasks. The architecture benefits from being able to incorporate Fuzzy, Neural Network and conventional System Identification methodology and experience. The advantages of the architecture are described, and the tradeoff between Local and Global Learning is investigated. The Local Learning method is computationally less expensive and was found to lead to smoother and more interpretable solutions than global learning. The results are illustrated with a robot actuator modelling problem.

[1]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[2]  H. Grosser.  Chicago , 1906 .

[3]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[4]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[5]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Chuen-Tsai Sun,et al.  Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.

[7]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[8]  T. A. Johansen,et al.  On local and fuzzy modelling , 1993, Third International Conference on Industrial Fuzzy Control and Intelligent Systems.

[9]  Léon Bottou,et al.  Local Learning Algorithms , 1992, Neural Computation.

[10]  Tor Arne Johansen,et al.  Adaptive Control of MIMO Non-Linear Systems Using Local ARX Models and Interpolation , 1994 .

[11]  W. Cleveland,et al.  Regression by local fitting: Methods, properties, and computational algorithms , 1988 .

[12]  Tor Arne Johansen,et al.  State-Space Modeling using Operating Regime Decomposition and Local Models , 1993 .

[13]  P. S. Lewis,et al.  Function approximation and time series prediction with neural networks , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[14]  James A. Cannatti,et al.  Vol. 78 , .

[15]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[16]  G. Wahba.  Spline models for observational data , 1990 .

[17]  Lyle H. Ungar,et al.  A NEURAL NETWORK ARCHITECTURE THAT COMPUTES ITS OWN RELIABILITY , 1992 .

[18]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.

[19]  T. Johansen,et al.  Identification of non-linear system structure and parameters using regime decomposition , 1994, Autom..

[20]  Volker Tresp,et al.  Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency , 1991, NIPS.

[21]  T. Johansen,et al.  Constructing NARMAX models using ARMAX models , 1993 .

[22]  Roderick Murray-Smith,et al.  Extending the functional equivalence of radial basis function networks and fuzzy inference systems , 1996, IEEE Trans. Neural Networks.

[23]  John A. Hertz,et al.  Exploiting Neurons with Localized Receptive Fields to Learn Chaos , 1990, Complex Syst..

[24]  Tor Arne Johansen,et al.  Nonlinear Predictive Control Using Local Models -Applied to a Batch Process , 1994 .

[25]  Lennart Ljung,et al.  Construction of Composite Models from Observed Data , 1992 .

[26]  Bjarne A. Foss,et al.  Representing and Learning Unmodeled Dynamics with Neural Network Memories , 1992, 1992 American Control Conference.

[27]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.