A duplication-free quantum neural network for universal approximation

A Wavelet Neural Network for Function Approximation and Network Optimization A WAVELET NEURAL NETWORK FOR FUNCTION APPROXIMATION AND NETWORK OPTIMIZATION

A new mapping network combined wavelet and neural networks is proposed. The algorithm consists of two process: the selfconstruction of networks and the minimization of errors. In the first process, the network structure is determined by using wavelet analysis. In the second process, the approximation errors are minimized. The merits of the proposed network are as follows: network optimization, ...

Verification of an Evolutionary-based Wavelet Neural Network Model for Nonlinear Function Approximation

Nonlinear function approximation is one of the most important tasks in system analysis and identification. Several models have been presented to achieve an accurate approximation on nonlinear mathematics functions. However, the majority of the models are specific to certain problems and systems. In this paper, an evolutionary-based wavelet neural network model is proposed for structure definiti...

On Universal Approximation Using Neural Networks

AppART: An ART Hybrid Stable Learning Neural Network for Universal Function Approximation

Universal Deep Neural Network Compression

Compression of deep neural networks (DNNs) for memoryand computation-efficient compact feature representations becomes a critical problem particularly for deployment of DNNs on resource-limited platforms. In this paper, we investigate lossy compression of DNNs by weight quantization and lossless source coding for memory-efficient inference. Whereas the previous work addressed non-universal scal...