다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Artificial neural networks: fundamentals, computing, design, and application

Theory of fuzzy integrals and its applications

Deep neural networks have been successfully used in diverse emerging domains to solve real world complex problems with may more deep learning(DL) architectures, being developed to date. To achieve these state-of-the-art performances, the DL architectures use activation functions (AFs), to perform diverse computations between the hidden layers and the output layers of any given DL architecture. This paper presents a survey on the existing AFs used in deep learning applications and highlights the recent trends in the use of the activation functions for deep learning applications. The novelty of this paper is that it compiles majority of the AFs used in DL and outlines the current trends in the applications and usage of these functions in practical deep learning deployments against the state-of-the-art research results. This compilation will aid in making effective decisions in the choice of the most suitable and appropriate activation function for any given application, ready for deployment. This paper is timely because most research papers on AF highlights similar works and results while this paper will be the first, to compile the trends in AF applications in practice against the research results from literature, found in deep learning research to date.

Activation Functions: Comparison of trends in Practice and Research for Deep Learning

A Note on Z-numbers

Sigmoid function is the most commonly known function used in feed forward neural networks because of its nonlinearity and the computational simplicity of its derivative. In this paper we discuss a variant sigmoid function with three parameters that denote the dynamic range, symmetry and slope of the function respectively. We illustrate how these parameters influence the speed of backpropagation learning and introduce a hybrid sigmoidal network with different parameter configuration in different layers. By regulating and modifying the sigmoid function parameter configuration in different layers the error signal problem, oscillation problem and asymmetrical input problem can be reduced. To compare the learning capabilities and the learning rate of the hybrid sigmoidal networks with the conventional networks we have tested the two-spirals benchmark that is known to be a very difficult task for backpropagation and their relatives.

The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning

A multilayer neural network based on multi-valued neurons (MLMVN) is considered in the paper. A multi-valued neuron (MVN) is based on the principles of multiple-valued threshold logic over the field of the complex numbers. The most important properties of MVN are: the complex-valued weights, inputs and output coded by the kth roots of unity and the activation function, which maps the complex plane into the unit circle. MVN learning is reduced to the movement along the unit circle, it is based on a simple linear error correction rule and it does not require a derivative. It is shown that using a traditional architecture of multilayer feedforward neural network (MLF) and the high functionality of the MVN, it is possible to obtain a new powerful neural network. Its training does not require a derivative of the activation function and its functionality is higher than the functionality of MLF containing the same number of layers and neurons. These advantages of MLMVN are confirmed by testing using parity n, two spirals and "sonar" benchmarks and the Mackey---Glass time series prediction.

Multilayer Feedforward Neural Network Based on Multi-valued Neurons (MLMVN) and a Backpropagation Learning Algorithm

A diffusion-neural-network for learning from small samples

Neural information processing models largely assume that the patterns for training a neural network are sufficient. Otherwise, there must exist a non-negligible error between the real function and the estimated function from a trained network. To reduce the error, in this paper, we suggest a diffusion-neural-network (DNN) to learn from a small sample consisting of only a few patterns. A DNN with more nodes in the input and layers is trained by using the deriving patterns instead of original patterns. In this paper, we give an example to show how to construct a DNN for recognizing a non-linear function. In our case, the DNN’s error is less than the error of the conventional BP network, about 48%. To substantiate the special case arguments, we also study other two non-linear functions with simulation technology. The results show that the DNN model is very effective in the case where the target function has a strong non-linearity or a given sample is very small.

This paper gives basics and reviews some classical as well as new applications of fuzzy logic. The main emphasis of the paper is on fuzzy decision making under a linguistic view of fuzzy sets.

http://facta.junis.ni.ac.rs/eae/fu2k52/moraga.pdf

Claudio Moraga

Papers

The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning

Multilayer Feedforward Neural Network Based on Multi-valued Neurons (MLMVN) and a Backpropagation Learning Algorithm

A diffusion-neural-network for learning from small samples

A diffusion-neural-network for learning from small samples

Introduction to Fuzzy Logic