650 research outputs found
On approximation properties of smooth fuzzy models
This Paper Addresses The Approximation Properties Of The Smooth Fuzzy Models. It Is Widely Recognized That The Fuzzy Models Can Approximate A Nonlinear Function To Any Degree Of Accuracy In A Convex Compact Region. However, In Many Applications, It Is Desirable To Go Beyond That And Acquire A Model To Approximate The Nonlinear Function On A Smooth Surface To Gain Better Performance And Stability Properties. Especially In The Region Around The Steady States, When Both Error And Change In Error Are Approaching Zero, It Is Much Desired To Avoid Abrupt Changes And Discontinuity In The Approximation Of The Input-Output Mapping. This Problem Has Been Remedied In Our Approach By Application Of The Smooth Compositions In The Fuzzy Modeling Scheme. In The Fuzzy Decomposition Stage Of Fuzzy Modeling, We Have Discretized The Parameters And Then Calculated The Result Through Partitioning Them Into A Dense Grid. This Could Enable Us To Present The Formulations By Convolution And Fourier Transformation Of The Parameters And Then Obtain The Approximation Properties By Studying The Structural Properties Of The Fourier Transformation And Convolution Of The Parameters. We Could Show That, Irrespective To The Shape Of The Membership Function, One Can Approximate The Dynamics And Derivative Of The Continuous Systems Together, Using The Smooth Fuzzy Structure. The Results Of The Paper Have Been Tested And Evaluated On A Discrete Event System In The Hybrid And Switched Systems Framework
Parameterized Convex Minorant for Objective Function Approximation in Amortized Optimization
Parameterized convex minorant (PCM) method is proposed for the approximation
of the objective function in amortized optimization. In the proposed method,
the objective function approximator is expressed by the sum of a PCM and a
nonnegative gap function, where the objective function approximator is bounded
from below by the PCM convex in the optimization variable. The proposed
objective function approximator is a universal approximator for continuous
functions, and the global minimizer of the PCM attains the global minimum of
the objective function approximator. Therefore, the global minimizer of the
objective function approximator can be obtained by a single convex
optimization. As a realization of the proposed method, extended parameterized
log-sum-exp network is proposed by utilizing a parameterized log-sum-exp
network as the PCM. Numerical simulation is performed for parameterized
non-convex objective function approximation and for learning-based nonlinear
model predictive control to demonstrate the performance and characteristics of
the proposed method. The simulation results support that the proposed method
can be used to learn objective functions and to find a global minimizer
reliably and quickly by using convex optimization algorithms.Comment: 12 pages, 4 figure
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
Parameter identification for piecewise-affine fuzzy models in noisy environment
AbstractIn this paper the problem of identifying a fuzzy model from noisy data is addressed. The piecewise-affine fuzzy model structure is used as non-linear prototype for a multiāinput, singleāoutput unknown system. The consequents of the fuzzy model are identified from noisy data which are collected from experiments on the real system. The identification procedure is formulated within the Frisch scheme, well established for linear systems, which is extended so that it applies to piecewise-affine, constrained models
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