Markov Chain and Adaptive Parameter Selection on Particle Swarm Optimizer

Chao-Wei Chou, Jiann-Horng Lin and Rong Jeng,I-Shou University,Taiwan

ABSTRACT

Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic behavior of the PSO is not easy. The choosing of parameters plays an important role since it is critical in the performance of PSO. As far as our investigation is concerned, most of the relevant researches are based on computer simulations and few of them are based on theoretical approach. In this paper, theoretical approach is used to investigate the behavior of PSO. Firstly, a state of PSO is defined in this paper, which contains all the information needed for the future evolution. Then the memory-less property of the state defined in this paper is investigated and proved. Secondly, by using the concept of the state and suitably dividing the whole process of PSO into countable number of stages (levels), a stationary Markov chain is established. Finally, according to the property of a stationary Markov chain, an adaptive method for parameter selection is proposed.

KEYWORDS

Markov chain, Memory-less property, Order Statistics, Particle Swarm Optimizer, Percentile, Stationary Markov chain

Original Source URL: https://airccse.org/journal/ijsc/papers/4213ijsc01.pdf

https://airccse.org/journal/ijsc/current2013.html

Call for Papers! May Issue!

International Journal on Soft Computing ( IJSC )

ISSN: 2229 - 6735 [Online] ; 2229 - 7103 [Print]

https://airccse.org/journal/ijsc/ijsc.html

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Here's where you can reach us : ijscjournal@yahoo.com or ijsc@aircconline.com

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Submission Deadline : April 16, 2022

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#Fuzzysystems #Neuralnetworks #Wavelet #Visionrecognition #Datavisualization #Telecommunications #Patternrecognition

Extracting Business Intelligence from Online Product Reviews

Soundarya.V, Siddareddy Sowmya Rupa, Sristi Khanna, G.Swathi and D.Manjula, Anna University, India

ABSTRACT

The project proposes to build a system which is capable of extracting business intelligence for a manufacturer, from online product reviews. For a particular product, it extracts a list of the discussed features and their associated sentiment scores. Online products reviews and review characteristics are extracted from www.Amazon.com. A two level filtering approach is adapted to choose a set of reviews that are perceived to be useful by customers. The filtering process is based on the concept that the reviewer generated textual content and other characteristics of the review, influence peer customers in making purchasing choices. The filtered reviews are then processed to obtain a relative sentiment score associated with each feature of the product that has been discussed in these reviews. Based on these scores, the customer's impression of each feature of the product can be judged and used for the manufacturers benefit.

Original Source URL: https://airccse.org/journal/ijsc/papers/4313ijsc02.pdf

https://airccse.org/journal/ijsc/current2013.html

Call for Papers...!!!

8th International Conference on Artificial Intelligence and Soft Computing (AIS 2022)

August 20 ~ 21, 2022, Chennai, India

https://csit2022.org/ais/index

Submission Deadline: April 09, 2022

Here's where you can reach us : ais@csit2022.org or aisconf@yahoo.com

https://csit2022.org/submission/index.php

A Survey of Spiking Neural Networks and Support Vector Machine Performance Byusinggpu's

Israel Tabarez-Paz1, Neil Hernandez-Gress2 and Miguel Gonzalez Mendoza2, 1Universidad Autonoma del Estado de Mexico, Mexico and 2Campus Estado de Mexico, Mexico

ABSTRACT

In this paper we study the performance of Spiking Neural Networks (SNN)and Support Vector Machine (SVM) by using a GPU, model GeForce 6400M. Respect to applications of SNN, the methodology may be used for clustering, classification of databases, odor, speech and image recognition..In case of methodology SVM, is typically applied for clustering, regression and progression. According to particular characteristics of these methodologies,theycan be parallelizedin several grades. However, level of parallelism is limited to architecture of hardware. So, is very sure to get better results using other hardware with more computational resources. The different approaches are evaluated by the training speed and performance. On the other hand, some authors have coded algorithms SVM light, but nobody has programming QP SVM in a GPU. Algorithms were coded by authors in the hardware, like Nvidia card, FPGA or sequential circuits that depends on methodology used, to compare learning timewith between GPU and CPU. Also, in the survey we introduce a brief description of the types of ANN and its techniques of execution to be related with results of researching.

KEYWORDS

GPU, Spiking Neural Networks, Support Vector Machines, pattern recognition.

Original Source URL: http://airccse.org/journal/ijsc/papers/4313ijsc01.pdf

https://airccse.org/journal/ijsc/current2013.html

#Fuzzysystems #Softcomputing #Neuralnetworks #Wavelet #Patternrecognition #Machinelearning