Comparing Imperialist Competitive Algorithm With Backpropagation Algorithms For Training Feedforward Neural Network
نویسندگان
چکیده
منابع مشابه
Identifying Flow Units Using an Artificial Neural Network Approach Optimized by the Imperialist Competitive Algorithm
The spatial distribution of petrophysical properties within the reservoirs is one of the most important factors in reservoir characterization. Flow units are the continuous body over a specific reservoir volume within which the geological and petrophysical properties are the same. Accordingly, an accurate prediction of flow units is a major task to achieve a reliable petrophysical description o...
متن کاملA successive overrelaxation backpropagation algorithm for neural-network training
A variation of the classical backpropagation algorithm for neural network training is proposed and convergence is established using the perturbation results of Mangasarian and Solodov. The algorithm is similar to the successive overrelaxation (SOR) algorithm for systems of linear equations and linear complementary problems in using the most recently computed values of the weights to update the ...
متن کاملImproved Cuckoo Search Algorithm for Feedforward Neural Network Training
The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. To enhance the accuracy and convergence rate of this algorithm, an improved cuckoo search algorithm is proposed in this paper. Normally, the parameters of the cuckoo search are kept constant. This may lead to decreasing the efficiency of the algorithm. ...
متن کاملA general backpropagation algorithm for feedforward neural networks learning
A general backpropagation algorithm is proposed for feedforward neural network learning with time varying inputs. The Lyapunov function approach is used to rigorously analyze the convergence of weights, with the use of the algorithm, toward minima of the error function. Sufficient conditions to guarantee the convergence of weights for time varying inputs are derived. It is shown that most commo...
متن کاملTraining Neural Networks Based on Imperialist Competitive Algorithm for Predicting Earthquake Intensity
In this study we determined neural network weights and biases by Imperialist Competitive Algorithm (ICA) in order to train network for predicting earthquake intensity in Richter. For this reason, we used dependent parameters like earthquake occurrence time, epicenter’s latitude and longitude in degree, focal depth in kilometer, and the seismological center distance from epicenter and earthquake...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Mathematics and Computer Science
سال: 2015
ISSN: 2008-949X
DOI: 10.22436/jmcs.014.03.02