Calibration Procedures for Indoor Location Using Fingerprinting

Mestre, Pedro; Reigoto, Luis; Coutinho, Luis; Correia, Aldina; Matias, João; Serôdio, Carlos

doi:10.1007/978-94-007-6190-2_27

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…The objective of this API is to be included in other software packages, to solve problems where derivative based methods cannot be used. It was used in location estimation problems by the authors in Mestre et al (2012Mestre et al ( , 2013 to tune the LEA (Location Estimation Algorithm) and adapt them to the mobile terminals. While in Mestre et al (2012) a Fuzzy Logic based LEA was implemented and the API was used to tune the parameters/transitions of membership functions and adjust the weights of OWA (Ordered Weighted Averaging), in Mestre et al (2013) the API was used to tune the internal parameters of the Weighted k-Nearest Neighbour algorithm and a scaling factor for the RSSI (Received Signal Strength) values.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Penalty and Barrier function based on Fuzzy Logic

Matias

Correia

Mestre

et al. 2015

Expert Systems with Applications

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Optimization methods have been used in many areas of knowledge, such as Engineering, Statistics, Chemistry, among others, to solve optimization problems. In many cases it is not possible to use derivative methods, due to the characteristics of the problem to be solved and/or its constraints, for example if the involved functions are non-smooth and/or their derivatives are not know. To solve this type of problems a Java based API has been implemented, which includes only derivative-free optimization methods, and that can be used to solve both constrained and unconstrained problems. For solving constrained problems, the classic Penalty and Barrier functions were included in the API. In this paper a new approach to Penalty and Barrier functions, based on Fuzzy Logic, is proposed. Two penalty functions, that impose a progressive penalization to solutions that violate the constraints, are discussed. The implemented functions impose a low penalization when the violation of the constraints is low and a heavy penalty when the violation is high. Numerical results, obtained using twentyeight test problems, comparing the proposed Fuzzy Logic based functions to six of the classic Penalty and Barrier functions are presented. Considering the achieved results, it can be concluded that the proposed penalty functions besides being very robust also have a very good performance.

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