Applying artificial immune system and ant algorithm in air-conditioner market segmentation

Chiu, Chih-Hui; Kuo, I-Ting; Lin, Chia-Hao

doi:10.1016/j.eswa.2008.05.005

Cited by 19 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It may be worthwhile to mention here that AIS have been successfully used in the past to study diverse areas of business. Examples of such areas include -its application in supply chain transportation problems [26]; market segmentation [27], financial pattern recognition and identification of financial atrisk companies [28], learning [29], intrusion detection in 'wi-fi' networks [30] etc. Although AIS encompasses several different types of algorithms, the most important ones are based upon Danger Theory, Clonal Selection, Immune Networks, and Negative Selection [25].…”

Section: Biological Perspectivementioning

confidence: 99%

Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification Theory and Case Study

Paul

Janecek

Neto³

et al. 2013

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence

View full text Add to dashboard Cite

In this paper, we propose a new methodology based on the Negative Selection Algorithm that belongs to the field of Computational Intelligence (specifically, Artificial Immune Systems -AIS) to identify takeover targets. Although considerable research based on customary statistical techniques and some contemporary Computational Intelligence techniques have been devoted to identify takeover targets, most of the existing studies are based upon multiple previous mergers and acquisitions. Contrary to previous research, the novelty of this proposal lies in the methodology's ability to suggest takeover targets for novice firms that are at the beginning of their merger and acquisition spree. We first discuss the theoretical perspective and then provide a case study with details for practical implementation, both capitalizing from unique generalization capabilities of AIS algorithms.

show abstract

Section: Biological Perspectivementioning

confidence: 99%

Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification Theory and Case Study

Paul

Janecek

Neto³

et al. 2013

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Zhang et al [9] provided a dynamic threshold artificial IA to improve the speed of convergence and to prevent premature convergence. Chiu et al [10] proposed an artificial immune system and ant algorithm for air conditioner market segmentation.…”

Section: Introductionmentioning

confidence: 99%

Using adaptive mutation to accelerate the convergence of immune algorithms for prediction of 3D molecular structure

Mansour

2015

International Journal of Computers and Applications

View full text Add to dashboard Cite

The prediction of the lowest energy 3D structure of a molecule is related to the global minimum of the molecular potential energy function. The search for the global minimum of this function is very difficult since the number of its local minimizers grows exponentially with the molecule size. In this paper, we adopt an efficient adaptive mutation approach to provide a solution to the problem of finding the optimal value of the mutation rate, p m , in order to fast the convergence of immune algorithms for minimizing the molecular potential energy function. This approach decreases the value of p m for high-fitness solutions to sustain the convergence capacity of the immune algorithms and increases the value of p m for low-fitness solutions to maintain diversity in the population.Computational results for problems with up to 300° of freedom are presented and are favorably compared with other existing methods from the literature. Also the results indicate that the proposed method is promising as it produces high-quality solutions with low computational costs.

show abstract

“…Lack of this affinity produces the elimination of such individuals. Immune network models have proven their use in problems that involve learning such as pattern recognition [55], clustering [56], and data mining [57], among others.…”

Section: Ais Techniquesmentioning

confidence: 99%

Development and evaluation of a fault detection and identification scheme for the WVU YF-22 UAV using the artificial immune system approach

Sanchez¹

View full text Add to dashboard Cite

A failure detection and identification (FDI) scheme is developed for a small remotely controlled jet aircraft based on the Artificial Immune System (AIS) paradigm. Pilot-in-the-loop flight data are used to develop and test a scheme capable of identifying known and unknown aircraft actuator and sensor failures. Negative selection is used as the main mechanism for self/non-self definition; however, an alternative approach using positive selection to enhance performance is also presented. Tested failures include aileron and stabilator locked at trim and angular rate sensor bias. Hyper-spheres are chosen to represent detectors. Different definitions of distance for the matching rules are applied and their effect on the behavior of hyper-bodies is discussed. All the steps involved in the creation of the scheme are presented including design selections embedded in the different algorithms applied to generate the detectors set. The evaluation of the scheme is performed in terms of detection rate, false alarms, and detection time for normal conditions and upset conditions. The proposed detection scheme achieves good detection performance for all flight conditions considered. This approach proves promising potential to cope with the multidimensional characteristics of integrated/comprehensive detection for aircraft subsystem failures. A preliminary performance comparison between an AIS based FDI scheme and a Neural Network and Floating Threshold based one is presented including groundwork on assessing possible improvements on pilot situational awareness aided by FDI schemes. Initial results favor the AIS approach to FDI due to its rather undemanding adaptation capabilities to new environments. The presence of the FDI scheme suggests benefits for the interaction between the pilot and the upset conditions by improving the accuracy of the identification of each particular failure and decreasing the detection delays. DEDICATION To my family for being always supportive in every decision I made and for helping me become who I am today. iii ACKNOWLEDGEMENTS First and foremost, I would like to thank Prof. Perhinschi and Prof. Napolitano for giving me the opportunity to further extend my college education. Specials thanks go to Prof. Perhinschi for letting me take part of this groundbreaking research giving me the best guidance one can expect from an Advisor. I would also like to thank Dr. Bojan Cukic for eagerly agreeing to form part of my committee and for providing positive feedback towards the successful completion of this work. My parents, Olga and Vicente, deserve a special thank for always letting me choose my own path and giving me an education one cannot learn from books; their generosity is out of imaginable limits. I would also like to express my sincere appreciation to my siblings Javier and Mariana for always believing in me even more than myself. I would also like to extend my gratitude to my friends, now spread throughout the world, for being the siblings one gets to choose to walk side by side through life, I am su...

show abstract

Applying artificial immune system and ant algorithm in air-conditioner market segmentation

Cited by 19 publications

References 13 publications

Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification Theory and Case Study

Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification Theory and Case Study

Using adaptive mutation to accelerate the convergence of immune algorithms for prediction of 3D molecular structure

Development and evaluation of a fault detection and identification scheme for the WVU YF-22 UAV using the artificial immune system approach

Contact Info

Product

Resources

About