[PDF] Top 20 A new class of wavelet networks for nonlinear system identification

... the system output that arises from the action of the th variable alone; the bivariate functional components represent the interacting contribution to the system output from the input vari- ables and , ... See full document

... defect identification and classification plays an important role in automated inspection of fabric ...visual system for the fabric defect detection ...visual system can catch only 60% to 70% of the ... See full document

... a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNN), is introduced for spatio- temporal system identification, by ... See full document

... a new method that combines Deep Neural Network (DNN) and Fourier series theory to accurately model and identify complex nonlinear system with multiple ...any nonlinear system with ... See full document

... NNs trained with the BP algorithm [14, 16] have, how- ever, two major drawbacks: first, their convergence is slow, which can be inadequate for online training; second, the NN parameters may be trapped in a nonoptimal ... See full document

... A new Ultra Least Squares (ULS) criterion is introduced for system ...the new ULS criterion is derived from the Sobolev space norm. The new criterion measures not only the discrepancy between ... See full document

... continuous wavelet transform (CWT) ...and nonlinear processes, the identification of faults in complex systems, applications in neural networks and differential equations, ... See full document

... a class of MIMO nonlinear uncertain non-affine ...for nonlinear systems [1, 4, ...the system functions must be exactly ...unstable system. Therefore, we propose an output-recurrent ... See full document

... thus enabling non-blocking communication between any pair of idle terminals through reassigning the exiting links. An N×N Benes network, which is also called B(k), can be constructed by adding one stage including N / 2 ( ... See full document

... where e is the number of edges and n is the number of nodes in the call graph (example in Figure 1). e is also the number of calls made, and n is also the number of routines. Therefore the level of private use is e. To ... See full document

... glass. System identification involves building mathematical models of a dynamic system based on a set of measured stimulus and response of sample ... See full document

... coupled system of nonlinear finite difference equations corresponding to a class of periodic- parabolic systems with time delays and with nonlinear boundary conditions in a bounded domain is ... See full document

... ABSTRACT Acute central nervous system (CNS) infections cause substantial morbidity and mortality, but the etiology remains unknown in a large proportion of cases. We identified and characterized the full genome of ... See full document

... the system, to reduce these losses, shunt capacitor banks are installed on distribution primary ...the system to achieve the above mentioned ...a system of equations in terms of new variables ... See full document

... the wavelet scale decreases and on the contrary, it increases with the frequency reduction of the wavelet ...continuous wavelet transform (CWT) is similar to FT and is expressed as follows: ... See full document

... The least intrusive and fastest biometric technology is the face recognition. This technology works with the recognition of human face. Unlike other recognition systems in which people need to place their hands on a ... See full document

... data system into an equivalent LTI discrete-time system with infinite-dimensional input- output signal ...sampled-data system is shown to be ... See full document

... odd nonlinear distortions are of similar magnitude, with the contri- butions dominating at the higher frequencies and the noise level is much lower (around -40dB) than the signal amplitude, indicating that the ... See full document

... This class uses three functional evaluations per cycle; however, its convergence order has not been determined. It depends on how the weight func- tion h(s) is chosen. In the following theorem, we address the ... See full document

... the nonlinear Hammerstein filter is a memoryless polynomial nonlinearity, we normalize (30) as in [25] and exercise con- trol over the change in the weight vector from one iteration to the next keeping the same ... See full document