International Journal of Man-Machine Studies
Signal flow graphs vs fuzzy cognitive maps in application to qualitative circuit analysis
International Journal of Man-Machine Studies
Using fuzzy cognitive maps as a system model for failure modes and effects analysis
Information Sciences: an International Journal
Soft Computing Techniques in Knowledge-Based Intelligent Engineering Systems: Approaches and Applications
Intelligent data analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A weight adaptation method for fuzzy cognitive map learning
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Quantization effects on the equilibrium behavior of combined fuzzy cognitive maps: Research Articles
International Journal of Intelligent Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Brain tumor characterization using the soft computing technique of fuzzy cognitive maps
Applied Soft Computing
CAKES-NEGO: Causal knowledge-based expert system for B2B negotiation
Expert Systems with Applications: An International Journal
Intelligent impact assessment of HRM to the shareholder value
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps
Information Sciences: an International Journal
Fuzzy cognitive map modelling educational software adoption
Computers & Education
A fuzzy cognitive map approach for effect-based operations: An illustrative case
Information Sciences: an International Journal
Benchmarking main activation functions in fuzzy cognitive maps
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Application of fuzzy cognitive maps for cotton yield management in precision farming
Expert Systems with Applications: An International Journal
A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps
Applied Soft Computing
Advanced soft computing diagnosis method for tumour grading
Artificial Intelligence in Medicine
Using fuzzy cognitive map for the relationship management in airline service
Expert Systems with Applications: An International Journal
Modeling uncertainty in clinical diagnosis using fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling complex systems using fuzzy cognitive maps
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Numerical and Linguistic Prediction of Time Series With the Use of Fuzzy Cognitive Maps
IEEE Transactions on Fuzzy Systems
Knowledge-Based Systems
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This work investigates the process of yield prediction in cotton crop production using the soft computing technique of fuzzy cognitive maps. Fuzzy cognitive map (FCM) is a fusion of fuzzy logic and cognitive map theories, and is used for modeling and representing experts' knowledge. It is capable of dealing with situations including uncertain descriptions using similar procedure such as human reasoning does. It is a challenging approach for decision making especially in complex processing environments. The FCM approach presented here was chosen to be utilized in agriculture because of the nature of the application. The prediction of yield in cotton production is a complex process with sufficient interacting parameters and FCMs are suitable for this kind of problem. Throughout this proposed method, FCMs designed and developed to represent experts' knowledge for cotton (Gossypium hirsutum L.) yield prediction and crop management. The developed FCM model consists of nodes linked by directed edges, where the nodes represent the main factors affecting cotton crop production such as texture, organic matter, pH, K, P, Mg, N, Ca, Na and cotton yield, and the directed edges show the cause-effect (weighted) relationships between the soil properties and cotton yield. The investigated methodology was evaluated for 360 cases measured during the time of six subsequent years (2001-2006) in a 5ha experimental cotton field, in predicting the yield class between two possible categories (''low'' and ''high''). The results obtained reveal its comparative advantage over the benchmarking machine learning algorithms tested for the same data set for the years mentioned by providing decisions that match better with the real measured ones. The main advantage of this approach is its simple structure and flexibility, representing knowledge visually and more descriptively. Hence, it might be a convenient tool in predicting cotton yield and improving crop management.