Optimization of Neural Network Architecture for Biomechanic Classification Tasks with Electromyogram Inputs

Optimization of Neural Network Architecture for Biomechanic Classification Tasks with Electromyogram Inputs

Electromyogram signals (EMGs) contain valuable information that can be used in man-machine interfacing between human users and myoelectric prosthetic devices. However, EMG signals are complicated and prove difficult to analyze due to physiological noise and other issues. Computational intelligence and machine learning techniques, such as artificial neural networks (ANNs), serve as powerful tool...

An Architecture of Neural Network for Fuzzy Teaching Inputs

A b s t r a c t A neural network for classifcation problems with linguistic terms is proposed. A fuzzy input is represented as a LR-type fizzy set. A generalized pocket algorithm, called f izzy pocket algorithm, that utilizes .IR-type fuzzy sets operations and defuzziJication method is first applied to train a linear threshold unit ( LTU ). This LTU node will classrfj, as many fuzzy input insta...

A Neural Network Architecture for Data Classification

This article aims at showing an architecture of neural networks designed for the classification of data distributed among a high number of classes. A significant gain in the global classification rate can be obtained by using our architecture. This latter is based on a set of several little neural networks, each one discriminating only two classes. The specialization of each neural network simp...

Neural Network with Matrix Inputs

In this paper we propose and analyze a multilayer perceptron-like model with matrix inputs. We applied the proposed model to the financial time series prediction problem, compared it with the standard multilayer perceptron model, and got fairly good results.

Convolutional Neural Network Visualization for fMRI Brain Disease Classification Tasks