Fuzzy Cognitive Map

In scenario planning, causal mapping has long been used as a means to elicit the worldviews of multiple experts, facilitate discussion, and challenge and improve mental models. Large and complex causal maps, however, are difficult to analyze. This paper proposes a novel method for scenario building, based on Fuzzy Cognitive Maps, that combines intuitive, cognitive mapping techniques with formal, quantitative analysis. The proposed method helps scenario planners to integrate the qualitative and partial knowledge of multiple individuals and overcome information processing limitations. The feasibility of the proposed approach is investigated with two scenario studies on solar photovoltaic panels.

Image Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. This paper presents a fuzzy rule base algorithm which is capable of detecting edges efficiently from the gray scale images. The following paper introduces such operators on hand of computer vision application. In the proposed algorithm, edginess at each pixel of a digital image is calculated using three 33 linear spatial filters i.e. low-pass, high-pass and edge enhancement (Sobel) filters through spatial convolution process. Finally We Have compared results of the proposed algorithm with other algorithms such as Sobel, Robert, and Prewitt. Experimental results show the ability and high performance of proposed algorithm

The notion of n-best approximations can be used for error correction in coding theory. The notion of n-eigen values can be used in deterministic modal superposition principle for undamped structures, which can find its applications in finite element analysis of mechanical structures with uncertain parameters. Further, it is suggested that the concept of n-matrices can be used in real world problems which adopts fuzzy models like Fuzzy Cognitive Maps, Fuzzy Relational Equations and Bidirectional Associative Memories. The applications of these algebraic structures are given in the third chapter. The fourth chapter suggests problems to further a reader's understanding of the subject.

Supply chain is a non-deterministic system in which uncontrollable external states with probabilistic behaviors (e.g., machine failure rate) influence on internal states (e.g., inventory level) significantly through complex causal relationships. Thanks to Radio frequency identification (RFID) technology, real time monitoring of the states is now possible. The current research on processing RFID data is, however, limited to statistical information. The goal of this research is to mine bidirectional cause-effect knowledge from the state data. In detail, fuzzy cognitive map (FCM) model of supply chain is developed. By using genetic algorithm, the weight matrix of the FCM model is discovered with the past state data, and forward (what-if) analysis is performed. Also, when sudden change in a certain state is detected, its cause is sought from the past state data throughout backward analysis. Simulation based experiments are provided to show the performance of the proposed forward–backward analysis methodology.

Cognitive maps have been used for analysing and aiding decision-making by investigating causal links among relevant domain concepts. A fuzzy cognitive map (FCM) is an extension of a cognitive map with the additional capability of representing feedback through weighted causal links. FCMs can be used as tools for both static as well as dynamic analysis of scenarios evolving with time. An FCM represents an expert's domain knowledge in a form that lends itself to relatively easy integration into a collective knowledge base for a group involved in a decision process. The resulting group FCM has the potential to serve as a useful tool in a group decision support environment. An appropriate methodology for the development and analysis of group FCMs is required. A framework for such a methodology consisting of the development and application phases is presented.

Cognitive maps have been used for analysing and aiding decision-making by investigating causal links among relevant domain concepts. A fuzzy cognitive map (FCM) is an extension of a cognitive map with the additional capability of representing feedback through weighted causal links. FCMs can be used as tools for both static as well as dynamic analysis of scenarios evolving with time. An FCM represents an expert's domain knowledge in a form that lends itself to relatively easy integration into a collective knowledge base for a group involved in a decision process. The resulting group FCM has the potential to serve as a useful tool in a group decision support environment. An appropriate methodology for the development and analysis of group FCMs is required. A framework for such a methodology consisting of the development and application phases is presented.

This paper proposes tree structured multi-layer fuzzy cognitive maps for modelling large-scale and complex real world problems and supporting the decision making process. Large-scale problems are characterized by a large number of parameters, concepts, variables, nonlinearities and uncertainties that make their analysis and modelling a very difficult task. The objective of the proposed methodology is to give an alternative approach

The responsiveness and efficiency of distributed and multi-echelon supply chains are increasingly reliant upon reduced cycle times within forward and reverse logistics operations. Within the high-tech industry, Reverse Logistics (RL) in the form of end-of-life, stock balancing, defective/warranty, recycle, and re-use product returns, is particularly important owing to the complexity and dependency that exist throughout the extended supply chain in order to maintain brand loyalty and customer satisfaction (source, OEM vendors and end customers alike). RL is fast becoming a strategy to acquire an organisational competitive advantage, making the quest to exploit such a function a premeditated decision. The introduction of Third-Party Logistics (3PL) providers has become a necessity for those supply-chain operations where issues of cost and risk mitigation need to be balanced with agility and responsiveness. This paper develops a conceptual framework of the factors influencing Reverse 3PL based upon Information Systems (IS) and associated Resource Commitment (RC) factors. The framework is then tested using a mixed-method research based approach using a 3PL organisation based in Thailand. The research findings reveal that among the proposed factors, IS- and RC-based operating performance, resource cost-effectiveness and RC (at financial, managerial and technological levels in the firm) were deemed to have a stronger positive relationship and input into reverse 3PL operations for the firm in context. The model is validated using the techniques of System Dynamics and Fuzzy Cognitive Mapping.