Journal ArticleDOI
An adaptive conjugate gradient learning algorithm for efficient training of neural networks
Hojjat Adeli,Shih-Lin Hung +1 more
TLDR
The problem of arbitrary trial-and-error selection of the learning and momentum ratios encountered in the momentum backpropagation algorithm is circumvented and the step length in the inexact line search is adapted during the learning process through a mathematical approach.About:
This article is published in Applied Mathematics and Computation.The article was published on 1994-04-01. It has received 121 citations till now. The article focuses on the topics: Adaptive algorithm & Population-based incremental learning.read more
Citations
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Journal ArticleDOI
Spiking neural networks.
TL;DR: A state-of-the-art review of the development of spiking neurons and SNNs is presented, and insight into their evolution as the third generation neural networks is provided.
Journal ArticleDOI
Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm
TL;DR: The experimental results show that PSOGSA outperforms both PSO and GSA for training FNNs in terms of converging speed and avoiding local minima and it is also proven that an FNN trained withPSOGSA has better accuracy than one trained with GSA.
Journal ArticleDOI
A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection
TL;DR: A new Multi-Spiking Neural Network (MuSpiNN) model is presented in which information from one neuron is transmitted to the next in the form of multiple spikes via multiple synapses and the model and learning algorithm employ the heuristic rules and optimum parameter values presented by the authors in a recent paper that improved the efficiency of the original single-spiking SNN model by two orders of magnitude.
Journal ArticleDOI
Improved spiking neural networks for EEG classification and epilepsy and seizure detection
TL;DR: It is concluded that RProp is the best training algorithm because it has the highest classification accuracy among all training algorithms specially for large size training datasets with about the same computational efficiency provided by SpikeProp.
Journal ArticleDOI
Enhanced probabilistic neural network with local decision circles: A robust classifier
Mehran Ahmadlou,Hojjat Adeli +1 more
TL;DR: An enhanced and generalized PNN (EPNN) is presented using local decision circles (LDCs) to overcome the aforementioned shortcoming of PNN and improve its robustness to noise in the data.
References
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Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Book
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
TL;DR: In this paper, Schnabel proposed a modular system of algorithms for unconstrained minimization and nonlinear equations, based on Newton's method for solving one equation in one unknown convergence of sequences of real numbers.
Book
Numerical methods for unconstrained optimization and nonlinear equations
TL;DR: Newton's Method for Nonlinear Equations and Unconstrained Minimization and methods for solving nonlinear least-squares problems with Special Structure.
Related Papers (5)
Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification
Hojjat Adeli,Xiaomo Jiang +1 more