Search results

1 – 10 of over 1000
Article
Publication date: 1 October 1995

Edward T. Lee and Te‐Shun Chou

The set of fuzzy threshold functions is defined to be a fuzzy set over the set of functions. All threshold functions have full memberships in this fuzzy set. Defines and…

Abstract

The set of fuzzy threshold functions is defined to be a fuzzy set over the set of functions. All threshold functions have full memberships in this fuzzy set. Defines and investigates a distance measure between a non‐linearly separable function and the set of all threshold functions. Defines an explicit expression for the membership function of a fuzzy threshold function through the use of this distance measure and finds three upper bounds for this measure. Presents a general method to compute the distance, an algorithm to generate the representation automatically, and a procedure to determine the proper weights and thresholds automatically. Presents the relationships among threshold gate networks, artificial neural networks and fuzzy neural networks. The results may have useful applications in logic design, pattern recognition, fuzzy logic, multi‐objective fuzzy optimization and related areas.

Details

Kybernetes, vol. 24 no. 7
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 10 April 2019

Zeno Toffano and François Dubois

The purpose of this paper is to apply the quantum “eigenlogic” formulation to behavioural analysis. Agents, represented by Braitenberg vehicles, are investigated in the context of…

Abstract

Purpose

The purpose of this paper is to apply the quantum “eigenlogic” formulation to behavioural analysis. Agents, represented by Braitenberg vehicles, are investigated in the context of the quantum robot paradigm. The agents are processed through quantum logical gates with fuzzy and multivalued inputs; this permits to enlarge the behavioural possibilities and the associated decisions for these simple vehicles.

Design/methodology/approach

In eigenlogic, the eigenvalues of the observables are the truth values and the associated eigenvectors are the logical interpretations of the propositional system. Logical observables belong to families of commuting observables for binary logic and many-valued logic. By extension, a fuzzy logic interpretation is proposed by using vectors outside the eigensystem of the logical connective observables. The fuzzy membership function is calculated by the quantum mean value (Born rule) of the logical projection operators and is associated to a quantum probability. The methodology of this paper is based on quantum measurement theory.

Findings

Fuzziness arises naturally when considering systems described by state vectors not in the considered logical eigensystem. These states correspond to incompatible and complementary systems outside the realm of classical logic. Considering these states allows the detection of new Braitenberg vehicle behaviours related to identified emotions; these are linked to quantum-like effects.

Research limitations/implications

The method does not deal at this stage with first-order logic and is limited to different families of commuting logical observables. An extension to families of logical non-commuting operators associated to predicate quantifiers could profit of the “quantum advantage” due to effects such as superposition, parallelism, non-commutativity and entanglement. This direction of research has a variety of applications, including robotics.

Practical implications

The goal of this research is to show the multiplicity of behaviours obtained by using fuzzy logic along with quantum logical gates in the control of simple Braitenberg vehicle agents. By changing and combining different quantum control gates, one can tune small changes in the vehicle’s behaviour and hence get specific features around the main basic robot’s emotions.

Originality/value

New mathematical formulation for propositional logic based on linear algebra. This methodology demonstrates the potentiality of this formalism for behavioural agent models (quantum robots).

Details

Kybernetes, vol. 48 no. 10
Type: Research Article
ISSN: 0368-492X

Keywords

Content available
Article
Publication date: 15 March 2017

Shih-Liang Chao and Ya-Lan Lin

This study has two purposes. The first is to identify the determinants influencing the selection of a container number recognition system via a quantitative method to thereby…

2995

Abstract

Purpose

This study has two purposes. The first is to identify the determinants influencing the selection of a container number recognition system via a quantitative method to thereby establish an evaluation structure. The second purpose is to conduct an empirical study to determine the weights of the criteria and alternatives.

Design/methodology/approach

The exploratory factor analysis (EFA) and fuzzy analytic hierarchy process (AHP) were applied to determine the evaluation structure and weights of the criteria and alternatives, respectively.

Findings

An empirical study based on a dedicated terminal at Keelung Port is conducted. The result demonstrates that the radio-frequency identification (RFID) system is a suitable system for the terminal under consideration in this study.

Originality/value

The value of this study is twofold. First, EFA was applied to extract common factors from a wide questionnaire survey, thereby establishing a hierarchical analysis structure. This method and comprehensive evaluation structure are useful references for both practitioners and researchers to deal with problems of gate automation. Second, fuzzy AHP was used to decide the weights of the hierarchical structure. The weights obtained by this method are more objective and rational as the imprecision expressions in returned samples have been considered and dealt with.

Details

Maritime Business Review, vol. 2 no. 1
Type: Research Article
ISSN: 2397-3757

Keywords

Article
Publication date: 1 April 2003

Edward T. Lee

A fuzzy symmetric threshold (ST) function is defined to be a fuzzy set over the set of functions. All ST functions have full memberships in this fuzzy set. For n variables, there…

251

Abstract

A fuzzy symmetric threshold (ST) function is defined to be a fuzzy set over the set of functions. All ST functions have full memberships in this fuzzy set. For n variables, there are (2n+2) ST functions. A distance measure between a nonsymmetric threshold function and the set of all ST functions is defined and investigated. An explicit expression for the membership function of a fuzzy ST function is defined through the use of this distance measure. An algorithm for obtaining this distance measure is presented with illustrative examples. It is also shown that any function and its complement always have the same grade of membership in the class of fuzzy ST functions. Applications to concise function representation and simple function implementation are also presented with examples. In addition, most inseparable unsymmetric functions are defined and investigated. Fuzzy ST functions are relevant to the development of practical applications of fuzzy methods and might contribute to the state of the art in the implementations of fuzzy methods in the areas requiring utilization of ST functions.

Details

Kybernetes, vol. 32 no. 3
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 1 December 1998

Traci May‐Plumlee and Trevor J. Little

Existing literature clearly documents the importance of new product development to success of a manufacturing firm. Many examples of generic models of the process, including…

2003

Abstract

Existing literature clearly documents the importance of new product development to success of a manufacturing firm. Many examples of generic models of the process, including sequential, concurrent, and multiple convergent models, can be found. However, these models are of insufficient detail to provide an adequate foundation for redesigning the apparel product development process. The no‐interval coherently phased product development (NICPPD) model for apparel introduced in this paper documents apparel product development as a six phase process with multiple convergent points and coherently phased divisions. The NICPPD model provides for developing both product lines and individual products, developing seasonal lines and multiple seasons annually, and use of alternative development strategies including original design development, knock‐offs or take‐offs, and modification of existing products. Multiple applications for use of the NICPPD model by both researchers and practitioners in examining and improving the apparel product development process are identified.

Details

International Journal of Clothing Science and Technology, vol. 10 no. 5
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 27 March 2009

Thomas Matheus

The purpose of this paper is to develop an integrated model, which incorporates the influence of different dimensions of power on various sub‐components of continuous innovation…

1630

Abstract

Purpose

The purpose of this paper is to develop an integrated model, which incorporates the influence of different dimensions of power on various sub‐components of continuous innovation in inter‐firm networks using the product development process (PDP) as the unit of analysis.

Design/methodology/approach

A theoretical framework is developed initially. The theoretical framework is supported by two illustrative examples from the aerospace industry. Semi‐structured interviews, observation and template analysis are proposed as suitable data collection and analysis methods.

Findings

The paper offers a view on how the PDP is facilitated and/or constrained due to this interweavement. The paper offers five tentative initial templates surrounding the themes discussed.

Research limitations/implications

The conceptual framework is still in its nascent stage and requires substantial empirical work. As the relationships between power and knowledge in inter‐firm networks are currently under‐researched it might be worthwhile considering a qualitative approach to widen our understanding of the interrelationships of the concepts before embarking on a quantitative research endeavour.

Originality/value

This paper provides a conceptual model of how four dimensions of power influence the integration of sub‐components of continuous innovation throughout the high‐phased stage‐gate process in an inter‐firm network.

Details

Management Research News, vol. 32 no. 3
Type: Research Article
ISSN: 0140-9174

Keywords

Open Access
Article
Publication date: 21 June 2022

Abhishek Das and Mihir Narayan Mohanty

In time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent…

Abstract

Purpose

In time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.

Design/methodology/approach

In this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.

Findings

The proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.

Research limitations/implications

Research in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.

Originality/value

The proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

Article
Publication date: 16 November 2021

Haneen Allataifeh, Sedigheh Moghavvemi and Jahan Ara Peerally

There is a lack of empirical-based models derived from practice to explain the digital innovation process. The authors investigate how the digital innovation process unfolds in…

Abstract

Purpose

There is a lack of empirical-based models derived from practice to explain the digital innovation process. The authors investigate how the digital innovation process unfolds in practice.

Design/methodology/approach

The authors undertake an exploratory and phenomenological study of 21 Malaysian small and medium enterprises (SMEs) in the information and communication technology (ICT) sector.

Findings

The findings show that the delineation between digital innovation process and outcome is blurred in practice, due to the process' iterative nature. Under this process, customers' role has changed from being passive receivers of innovative products to active reviewers, testers, influential decision-makers, initiators and co-creators at different review points in the innovation process. Enterprises' role has expanded from being the initiator of the innovation process to being a cogitative actor by seeking and absorbing knowledge from customer reviews into the digital innovation process. Market analysis is often the initiator of the digital innovation process, and the findings shed light on the underlying causative mechanisms of the initiation stage, which are understudied and not well understood in the existing literature.

Originality/value

The study contributes to academic knowledge by answering scholars' call for developing third-generation practice-based innovation models, which accounts for enterprises' context-specificities and internal and external environments, and for exploring the suitability of the need–solution fit approach for the digital innovation process. Such models have only been conceptually advocated in the literature. The study also informs practitioners on the organizational and operational activities involved in managing and strategizing for the digital innovation process.

Details

European Journal of Innovation Management, vol. 26 no. 3
Type: Research Article
ISSN: 1460-1060

Keywords

Article
Publication date: 7 September 2015

Kanda Boonsothonsatit, Sami Kara, Suphunnika Ibbotson and Berman Kayis

The purpose of this paper is to propose a Generic decision support system which is based on multi-Objective Optimisation for Green supply chain network design (GOOG). It aims to…

Abstract

Purpose

The purpose of this paper is to propose a Generic decision support system which is based on multi-Objective Optimisation for Green supply chain network design (GOOG). It aims to support decision makers to design their supply chain networks using three key objectives: the lowest cost and environmental impact and the shortest lead time by incorporating the decision maker’s inputs.

Design/methodology/approach

GOOG aims to suggest the best-fitted parameters for supply chain partners and manufacturing plant locations, their order allocations, and appropriate transportation modes and lot-sizes for cradle-to-gate. It integrates Fuzzy Goal Programming and weighted max-min operator for trade-off conflicting objectives and overcome fuzziness in specifying target values of individual objectives. It is solved using exact algorithm and validated using an industrial case study.

Findings

The comparative analysis between actual, three single-objective, and multi-objective decisions showed that GOOG is capable to optimising three objectives namely cost, lead time, and environmental impact.

Research limitations/implications

Further, GOOG requires validation for different supply chain scenarios and manufacturing strategic decisions. It can improve by including multi-echelon supply chain networks, entire life cycle and relevant environmental legislations.

Practical implications

GOOG helps the decision makers to configuring those supply chain parameters whilst minimising those three objectives.

Social implications

Companies can use GOOG as a tool to strategically select their supply chain that reduces their footprint and stop rebound effect which imposes significant impact to the society.

Originality/value

GOOG includes overlooked in the previous study in order to achieve the objectives set. It is flexible for the decision makers to change the relative weightings of the inputs for those contradicting objectives.

Details

Journal of Manufacturing Technology Management, vol. 26 no. 7
Type: Research Article
ISSN: 1741-038X

Keywords

Article
Publication date: 15 June 2010

Jian Jin, Dao‐Lei Liang, Yu Bao and Guo‐Xing Huang

The purpose of this paper is to present a committee machine (CM) with two‐layer expert nets to overcome the lack of approximating ability of CM with single‐layer expert nets.

Abstract

Purpose

The purpose of this paper is to present a committee machine (CM) with two‐layer expert nets to overcome the lack of approximating ability of CM with single‐layer expert nets.

Design/methodology/approach

A frequently used structure of CM, with a fuzzy c‐means clustering algorithm as splitting and combining unit and some single‐layer linear neural nets as expert modules, was applied to short‐term climate prediction. Considering the complexity of the climate conditions, use was made of two‐layer back propagation (BP) neural nets instead of single‐layer linear nets to test the effect of the model. Experiments were performed on both synthetic and realistic climatic data.

Findings

Prediction accuracy is raised when the BP nets were used and as the number of hidden neurons increased at some stages. It implies that improving the approximating ability of individual expert module of a CM is beneficial.

Research limitations/implications

The optimal learning rate, the optimal cluster numbers and the maximal number of iteration were not well treated.

Practical implications

The paper is a useful alternative worth consideration for the complicated prediction problems.

Originality/value

A CM with two‐layer expert nets are presented. Comparisons are made between CMs with simple and complex expert nets.

Details

Kybernetes, vol. 39 no. 6
Type: Research Article
ISSN: 0368-492X

Keywords

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