AIMC Topic: Neural Networks, Computer

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Improvement of Neural-Network Classifiers Using Fuzzy Floating Centroids.

IEEE transactions on cybernetics
In this article, a fuzzy floating centroids method (FFCM) is proposed, which uses a fuzzy strategy and the concept of floating centroids to enhance the performance of the neural-network classifier. The decision boundaries in the traditional floating ...

Toward Efficient Processing and Learning With Spikes: New Approaches for Multispike Learning.

IEEE transactions on cybernetics
Spikes are the currency in central nervous systems for information transmission and processing. They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to th...

A Deep Learning Approach for Recognizing the Cursive Tamil Characters in Palm Leaf Manuscripts.

Computational intelligence and neuroscience
Tamil is an old Indian language with a large corpus of literature on palm leaves, and other constituents. Palm leaf manuscripts were a versatile medium for narrating medicines, literature, theatre, and other subjects. Because of the necessity for dig...

Application Analysis of Combining BP Neural Network and Logistic Regression in Human Resource Management System.

Computational intelligence and neuroscience
Human resource management involves a variety of data processing, and the process is complicated. In order to improve the effect of human resource management, this paper combines BP neural network and logistic regression analysis to construct an intel...

An Algorithm for Time Prediction Signal Interference Detection Based on the LSTM-SVM Model.

Computational intelligence and neuroscience
Interference detection is an important part of the electronic defense system. It is difficult to detect interference with the traditional method of extracting characteristic parameters for interference generated at the same frequency as the original ...

Linking Brain Structure, Activity, and Cognitive Function through Computation.

eNeuro
Understanding the human brain is a "Grand Challenge" for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generati...

Deep Multi-Scale Fusion of Convolutional Neural Networks for EMG-Based Movement Estimation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
EMG-based motion estimation is required for applications such as myoelectric control, where the simultaneous estimation of kinematic information, namely joint angle and velocity, is challenging and critical. We propose a novel method for accurately m...

An Empirical Evaluation of Network Representation Learning Methods.

Big data
Network representation learning methods map network nodes to vectors in an embedding space that can preserve specific properties and enable traditional downstream prediction tasks. The quality of the representations learned is then generally showcase...

Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network.

Environmental pollution (Barking, Essex : 1987)
Water quality forecasting can provide useful information for public health protection and support water resources management. In order to forecast water quality more accurately, this paper proposes a novel hybrid model by combining data decomposition...

A differential Hebbian framework for biologically-plausible motor control.

Neural networks : the official journal of the International Neural Network Society
In this paper we explore a neural control architecture that is both biologically plausible, and capable of fully autonomous learning. It consists of feedback controllers that learn to achieve a desired state by selecting the errors that should drive ...