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Book part
Publication date: 5 October 2018

Nima Gerami Seresht, Rodolfo Lourenzutti, Ahmad Salah and Aminah Robinson Fayek

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes…

Abstract

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Details

Fuzzy Hybrid Computing in Construction Engineering and Management
Type: Book
ISBN: 978-1-78743-868-2

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Article
Publication date: 1 September 2005

S.A. Oke, A.O. Johnson and O.O. Omogoroye

The purpose of this paper is to present a new approach in viewing the control of safety at crude oil exploration platforms.

Abstract

Purpose

The purpose of this paper is to present a new approach in viewing the control of safety at crude oil exploration platforms.

Design/methodology/approach

The approach utilized in this work is the fusion of artificial neural network and fuzzy logic. The approach is adopted in view of the better presentation of solutions to the safety control problem that neuro‐fuzzy exhibits. It is better than the individual application of either artificial neural network or fuzz logic to the problem at hand. The model captures uncertainties and imprecision that are prevalent in the quantification or data gathering stage of safety control measurement.

Findings

It was demonstrated that the application of neuro‐fuzzy is feasible. The results seem applicable to similar settings with similar system characteristics.

Practical implications

Since more confidence is obtained with the use of this more effective tool, there is improvement in decision making based on reliance on the model. Thus, the improved quality of decision made would positively affect lives of workers at the oil platforms or the materials or equipment used for exploration purposes.

Originality/value

The work is original in that it is the first time the neuro‐fuzzy methodology would be applied to offshore oil platform safety control.

Details

Disaster Prevention and Management: An International Journal, vol. 14 no. 4
Type: Research Article
ISSN: 0965-3562

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Article
Publication date: 9 May 2008

Servet Tuncer and Beşir Dandil

The paper aims to propose an adaptive and robust on‐line trained neuro‐fuzzy current controller based on indirect field oriented control (IFOC) for the current control of…

Abstract

Purpose

The paper aims to propose an adaptive and robust on‐line trained neuro‐fuzzy current controller based on indirect field oriented control (IFOC) for the current control of multilevel inverter fed induction motor (IM).

Design/methodology/approach

Torque current of IM is controlled with Sugeno type neuro‐fuzzy controller (NFC) which has the ability of self tuning against parameter variations and load disturbance. Input variables of the neuro‐fuzzy current controller are chosen error and integral of error in order to eliminate steady state error. The consequent parameters of neuro‐fuzzy current controller are trained on‐line through backpropagation learning algorithm.

Findings

The validity of proposed current control algorithm is shown with experimental results carried out under different speed commands, parameter variations and load disturbances. The experimental results show that control performance of NFC in the current control of IMs is satisfactory because of its adaptive and robust structure.

Originality/value

This paper presents the design of an on‐line trained neuro‐fuzzy current control to improve the current control performance. The performance of the current controller largely depends on using converter systems. In this study, a multilevel inverter is used to obtain less harmonic distortion and near sinusoidal form of output voltage and current waveforms of the converter.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 27 no. 3
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 1 June 2005

Manish Kumar and Devendra P. Garg

The paper aims to advance methodologies to optimize fuzzy logic controller parameters via neural network and use the neuro‐fuzzy scheme to control two cooperating robots.

Abstract

Purpose

The paper aims to advance methodologies to optimize fuzzy logic controller parameters via neural network and use the neuro‐fuzzy scheme to control two cooperating robots.

Design/methodology/approach

The paper presents a special neural network architecture that can be converted to fuzzy logic controller. Concepts of model predictive control (MPC) have been used to generate optimal signal to be used to train the neural network via backpropagation. Subsequently, a trained neural network is used to obtain fuzzy logic controller parameters.

Findings

The proposed neuro‐fuzzy scheme is able to precisely learn the control relation between input‐output training data generated by the learning algorithm. From the experiments performed on the industrial grade robots at Robotics and Manufacturing Automation (RAMA) Laboratory, it was found that the neuro‐fuzzy controller was able to learn fuzzy logic rules and parameters accurately.

Research limitations/implications

The backpropagation method, used in this research, is extremely dependent on initial choice of parameters, and offers no mechanism to restrict the parameters within specified range during training. Use of alternative learning mechanisms, such as reinforcement learning, needs to be investigated.

Practical implications

The neuro‐fuzzy scheme presented can be used to develop controller for plants for which it is difficult to obtain analytical model or sufficient information about input‐output heuristic relation is not available.

Originality/value

The paper presents the neural network architecture and introduces a learning mechanism to train this architecture online.

Details

Industrial Robot: An International Journal, vol. 32 no. 3
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 25 January 2018

Hima Bindu and Manjunathachari K.

This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days…

Abstract

Purpose

This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend.

Design/methodology/approach

This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database.

Findings

The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01.

Originality/value

This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

Details

Sensor Review, vol. 38 no. 3
Type: Research Article
ISSN: 0260-2288

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Article
Publication date: 14 August 2007

Tomasz Pajchrowski, Krzysztof Zawirski and Stefan Brock

The purpose of the paper is to find a simple structure of speed controller robust against drive parameter variations. Application of neuro‐fuzzy technique in the…

Abstract

Purpose

The purpose of the paper is to find a simple structure of speed controller robust against drive parameter variations. Application of neuro‐fuzzy technique in the controller of PI type creates proper nonlinear characteristics, which ensures controller robustness.

Design/methodology/approach

The robustness of the controller is based on its nonlinear characteristic introduced by neuro‐fuzzy technique. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the neuro‐fuzzy system to form the proper shape of the control surface, which represents the nonlinear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behavior of a laboratory speed control system is validated in the experimental setup. The control algorithms of the system are performed by a microprocessor floating point DSP control system.

Findings

The proposed controller structure with proper control surface created by the neuro‐fuzzy technique guarantees expected robustness.

Research limitations/implications

The proposed controller was tested on a single machine under well defined conditions. Further investigations are required before any industrial applications can be made.

Practical implications

The proposed controller synthesis and its results may be very helpful in the robotic system where changing of system parameters is characteristic for many industrial robots and manipulators.

Originality/value

The original method of robust controller synthesis was proposed and validated by simulation and experimental investigations.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 26 no. 4
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 4 March 2014

Ahmad Mozaffari, Alireza Fathi and Saeed Behzadipour

The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults…

Abstract

Purpose

The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits.

Design/methodology/approach

In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and a swarm-based explorer with adaptive fuzzified parameters (SBEAFP). Thereafter, a revised version of the group method data handling (GMDH) policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner.

Findings

Based on comparative numerical experiments, the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments. It is proved that the method outperforms some well-known classifiers such as support vector machine (SVM) and particle swarm optimization-based SVM (PSO-SVM). Besides, it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier. For the case, it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities, and consequently optimize the structure of SONeFMUC.

Originality/value

The originality of the paper can be considered from both numerical and practical points of view. The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults, i.e. cylinder fault, pump fault, valve leakage fault and rupture of the piping system. Besides, to elaborate on the authenticity and efficacy of the proposed method, its performance is compared with well-known rival techniques.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 7 no. 1
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 6 March 2019

Saleeshya P.G. and Binu M.

Lean implementation is a strategic decision. The capacity of organisation to be “Lean” can be identified before lean implementation by assessing leanness of an…

Abstract

Purpose

Lean implementation is a strategic decision. The capacity of organisation to be “Lean” can be identified before lean implementation by assessing leanness of an organisation. This study aims to attempt developing a holistic leanness assessment tool for assessing organisational leanness.

Design/methodology/approach

A neuro-fuzzy leanness assessment model for assessing the leanness of a manufacturing system is presented. The model is validated academically and industrially by conducting a case study.

Findings

Neuro-fuzzy hybridisation helped assess the leanness accurately. Fuzzy logic helped to perform the leanness assessment more realistically by accounting ambiguity and vagueness in organisational functioning and decision-making processes. Neural network increased the learning capacity of assessment model and increased the accuracy of leanness index.

Research limitations/implications

The industrial case study in the paper shows the results in telecom equipment manufacturing industry. This may not represent entire manufacturing sector. The generic nature of the model developed in this research ensures its wide applicability.

Practical implications

The neuro-fuzzy hybrid model for assessing leanness helps to identify the potential of an organisation to become “Lean”. The organisational leanness index developed by the study helps to monitor the effectiveness and impact of lean implementation programmes.

Originality/value

The leanness assessment models available in literature lack depth and coverage of leanness parameters. The model developed in this research assesses leanness of an organisation by accounting for leanness aspects of inventory management, industrial scheduling, organisational flexibility, ergonomics, product, process, management, workforce, supplier relationship and customer relationship with the help of neuro-fuzzy hybrid modelling.

Details

International Journal of Lean Six Sigma, vol. 10 no. 1
Type: Research Article
ISSN: 2040-4166

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Article
Publication date: 1 September 2000

K. Rashid, J.A. Ramírez and E.M. Freeman

Many engineering optimisation problems are difficult to describe mathematically and as such can not be easily optimised. Recently attention has focussed on developing…

Abstract

Many engineering optimisation problems are difficult to describe mathematically and as such can not be easily optimised. Recently attention has focussed on developing methods to create approximations of the real object function using numerical model data instead. The approximated function can then be optimised using a suitable optimisation method. This paper describes the extraction of derivative information from a neuro‐fuzzy system. Subsequently, this permits the application of classic deterministic optimisation methods in order to identify the global minimum of any approximated objective function. For non‐differentiable functions this approach is of great benefit. Results from an analytical optimisation example, in which the objective function and the solution are known, and a two variable loudspeaker optimisation problem are discussed. In both cases, the neuro‐fuzzy system worked well to model the physical problem and the extracted derivative served to locate the minimum.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 19 no. 3
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 5 June 2007

Jui‐Lin Wang, Chien‐Ta Ho, Chin‐Shien Lin and Shihyu Chou

This research, based on the idea of contingency fit, attempts to construct a strategic selection system to select the best fit competency of channel strategic selection by…

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Abstract

Purpose

This research, based on the idea of contingency fit, attempts to construct a strategic selection system to select the best fit competency of channel strategic selection by using neuro‐fuzzy technique.

Design/methodology/approach

Neuro‐fuzzy technique is used.

Findings

In addition to, providing a concrete bilateral solution to select the right and better fit competency of channel strategy and giving an alternative way to explain fit, this strategic selection system can also be used as a competency diagnosis system for channel members in the supply chain organization, providing clues for further channel performance promotion and channel modernization.

Originality/value

Through the literature and construction of strategy channel satisfaction model (SCSM) concepts and ideas, an empirical study of the Asian Commerce Community meet distribution channel relationships will pose an Asian developed channel modernization perspective of SCSM to discuss the critical marketing strategy selection for the traditional orient “commercial channel”.

Details

Benchmarking: An International Journal, vol. 14 no. 3
Type: Research Article
ISSN: 1463-5771

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