In this work, we show that fuzzy inference systems based on Similarity Based Reasoning (SBR) where the modification function is a fuzzy implication is a universal approximator under suitable conditions on the other components of the fuzzy system.

A perception-based logical deduction is formulated in the frame of fuzzy intensional logic. We will show that under certain conditions, this kind of deduction is also a universal approximator.

Neural network process modelling needs the use of experimental design and studies. A new neural network constructive algorithm is proposed. Moreover, the paper deals with the influence of the parameters of radial basis function neural networks and multilayer perceptrons network in process modelling. Particularly, it is shown that the neural modelling, depending on learning approach, cannot be a...

Based on the superiority of piezoelectric elements, including lightweight, high electric mechanical transformation efficiency and a quick response time, piezoelectric-based micro-positioning actuator is developed in this investigation. For eliminating effects hysteresis modeling uncertainties that appeared actuator, nonlinear adaptive fuzzy robust control design with perturbation cancellation a...

Abstract This paper presents an investigation of the approximation property of neural networks with unbounded activation functions, such as the rectified linear unit (ReLU), which is the new de-facto standard of deep learning. The ReLU network can be analyzed by the ridgelet transform with respect to Lizorkin distributions. By showing three reconstruction formulas by using the Fourier slice the...

Yu-Zhong Chen and Ying-Cheng Lai 2, ∗ School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA Department of Physics, Arizona State University, Tempe, Arizona 85287, USA Abstract Revealing the structure and dynamics of complex networked systems from observed data is of fundamental importance to science, engineering, and society. Is it possible t...

This paper proposes a specific type of Local Linear Model, the Shuffled Linear Model (SLM), that can be used as a universal approximator. Local operating points are chosen randomly and linear models are used to approximate a function or system around these points. The model can also be interpreted as an extension to Extreme Learning Machines with Radial Basis Function nodes, or as a specific wa...

This paper presents a learning algorithm for a VMM + WTA classifier one layer architecture on a Large-Scale Field Programmable Analog Array (FPAA). The technique enables opportunities for embedded, ultra-low power machine learning, techniques typically considered for large servers. To develop this training algorithm, the paper starts by understanding fundamental equivalent transformations for V...

The human liver–bile system is a complex, non-linear system. A soft-computing based universal approximator, the singleton based Product-Sum-Gravity inference is applied as a modeling technique. As the available information about the system is very limited, an additional technique, the partition refinement is used in the training process. Higher Order SVD was chosen for complexity reduction. The...