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2002
Particle swarm is an optimization paradigm for real-valued functions, based on the social dynamics of group interaction. We propose its application to the training of neural networks. Comparative tests were carried out, for classification and regression tasks
Journal of Computational Science
The optimal combination: Grammatical swarm, particle swarm optimization and neural networks2012 •
2011 •
Abstract Incremental social learning (ISL) was proposed as a way to improve the scalability of systems composed of multiple learning agents. In this paper, we show that ISL can be very useful to improve the performance of population-based optimization algorithms.
Journal of Physics: Conference Series
Particle swarm optimization versus gradient based methods in optimizing neural networkNeural network modelling has become a great interest for many statisticians to be utilized in various types of data as classification, regression, and time series. It also has been applied in many fields as environment, financial, medical, agriculture and climate change. A lot of parametric methods have been developed to predict time series data such as ARIMA and exponential smoothing. However, requirement of residual assumptions causes limitedness of the models. Time series prediction by using neural network been an interesting study in the forecasting problem. In this model, one of the most interesting discussion is about how to get the optimal weights. Various gradient and non-gradient based methods have been applied in obtaining the network weights. Particle swarm optimization is one non-gradient based algorithm inspired by the behaviour of birds and fish flocks, which move to form certain formations without colliding to get the best position in a multi-dimensional space. In neu...
Brazilian Symposium on Neural Networks
An Analysis Of PSO Hybrid Algorithms For Feed-Forward Neural Networks Training2006 •
Training neural networks is a complex task of great importance in problems of supervised learning. The Particle Swarm Optimization (PSO) consists of a stochastic global search originated from the attempt to graphically simulate the social behavior of a flock of birds looking for resources. In this work we analyze the use of the PSO algorithm and two variants with a
2016 •
Swarm Computation is a relatively new optimisation paradigm. The basic premise is to model the collective behaviour of self-organised natural phenomena such as swarms, flocks and shoals, in order to solve optimisation problems. Particle Swarm Optimisation (PSO) is a type of swarm computation inspired by bird flocks or swarms of bees by modelling their collective social influence as they search for optimal solutions. In many real-world applications of PSO, the algorithm is used as a data pre-processor for a neural network or similar post processing system, and is often extensively modified to suit the application. The thesis introduces techniques that allow unmodified PSO to be applied successfully to a range of problems, specifically three extensions to the basic PSO algorithm: solving optimisation problems by training a hyperspatial matrix, using a hierarchy of swarms to coordinate optimisation on several data sets simultaneously, and dynamic neighbourhood selection in swarms. Rath...
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Ani and Georgia. STUDIES • MATERIALS
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Zenodo (CERN European Organization for Nuclear Research)
Quando objectos de colecção falam das (tele)comunicações2005 •
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2024 •
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Earthquake Engineering & Structural Dynamics
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International Journal of Molecular Sciences
Cocaprins, β-Trefoil Fold Inhibitors of Cysteine and Aspartic Proteases from Coprinopsis cinerea