Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links
International Journal of Human-Computer Studies
Brain tumor characterization using the soft computing technique of fuzzy cognitive maps
Applied Soft Computing
Computers and Electronics in Agriculture
Benchmarking main activation functions in fuzzy cognitive maps
Expert Systems with Applications: An International Journal
Prediction of street tree morphological parameters using artificial neural networks
Computers and Electronics in Agriculture
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
Genetic learning of fuzzy cognitive maps
Fuzzy Sets and Systems
Review: Development of soft computing and applications in agricultural and biological engineering
Computers and Electronics in Agriculture
A neural network experiment on the site-specific simulation of potato tuber growth in Eastern Canada
Computers and Electronics in Agriculture
Expert Systems with Applications: An International Journal
Modeling complex systems using fuzzy cognitive maps
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Learning Algorithms for Fuzzy Cognitive Maps—A Review Study
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Delineation of management zones in an apple orchard in Greece using a multivariate approach
Computers and Electronics in Agriculture
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This work investigates the yield modeling and prediction process in apples (cv. Red Chief) using the dynamic influence graph of Fuzzy Cognitive Maps (FCMs). FCMs are ideal causal cognition tools for modeling and simulating dynamic systems. They gained momentum due to their simplicity, flexibility to model design, adaptability to different situations, and easiness of use. In general, they model the behavior of a complex system, have inference capabilities and can be used to predict new knowledge. In this work, a data driven non-linear FCM learning approach was chosen to categorize yield in apples, where very few decision making techniques were investigated. Through the proposed methodology, FCMs were designed and developed to represent experts' knowledge for yield prediction and crop management. The developed FCM model consists of nodes linked by directed edges, where the nodes represent the main soil factors affecting yield, [such as soil texture (clay and sand content), soil electrical conductivity (EC), potassium (K), phosphorus (P), organic matter (OM), calcium (Ca) and zinc (Zn) contents], and the directed edges show the cause-effect (weighted) relationships between the soil properties and yield. The main purpose of this study was to classify apple yield using an efficient FCM learning algorithm, the non-linear Hebbian learning, and to compare it with the conventional FCM tool and benchmark machine learning algorithms. All algorithms have been implemented in the same data set of 56 cases measured in 2005 in an apple orchard located in central Greece. The analysis showed the superiority of the FCM learning approach in yield prediction.