By Peter P. Groumpos (auth.), Michael Glykas (eds.)
The thought of cognitive maps used to be constructed in 1976. Its major objective was once the illustration of (causal) relationships between “concepts” often referred to as “factors” or “nodes”. options will be assigned values. Causal relationships among suggestions might be of 3 forms: optimistic, destructive or impartial. raise within the worth of an idea could yield a corresponding confident or unfavourable raise on the strategies attached to it through relationships. In 1986 Bart Kosko brought the proposal of fuzziness to cognitive maps and created the idea of Fuzzy Cognitive Maps (FCMs). the connection among suggestions in (FCMs) can take a price within the period [-1,1]. This dating price is termed “weight”. For the final two decades broad examine within the thought of FCMs has been played that supplied significant advancements and improvements in its theoretical underpinning. New methodologies and methods were constructed. FCMs have additionally been utilized to many alternative sectors. New software program instruments were constructed that automate FCM construction and administration. the purpose of this e-book is to provide fresh advances and cutting-edge in FCM conception, methodologies, functions and instruments that exist so far scattered in magazine papers, in a concrete and built-in manner.
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Additional info for Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications
Except the experts’ knowledge for determining FCM model, quantitative data for the eight main histopathological features [13,15] were also available and used for constructing DT. Then through the inductive learning procedure, a set of association rules were derived. Some of the best association rules, based on their confidence levels, are given in the Table 2. The necessary IfThen rules were induced and introduced in the FCM model enhancing its initial structure. P. Groumpos Table 2 Example of Association Rules derived from Decision Trees Rules Cell-size=uniform, mitosis=absent rate Cell-distribution=even, nucleoli=inconspicuous Cell-distribution=clustered, cellsize=pleomorphic Nuclei=uniform, mitosis=absent rate Cell-size=uniform, cell-number=numerous, nucleoli=inconspicuous Result/Decis ion Leaves Grade Low Grade Low Grade High Grade Low Grade Low After the development of the DT-FCM model and the determination of specifications for the implementation of the NHL algorithm, the hybrid system was used to examine cases and assigned sensitivity and specificity for grading bladder tumors.
G. Stach et al. 2004a), to a statement that the structurally different maps can generate very similar simulations. , solutions with low simulation-error and relatively high matrix-error, decrease 36 W. Stach, L. Kurgan, and W. 250 0 Fig. 6 FCM model of the process control system learned using RCGA method Fig. 7 Simulation result of the FCM for the process control system learned using the RCGA method as the amount of the available input data increases (see Stach et al. 2004a for details). One has to be aware that suboptimal solutions can be generated by the fully automated learning approaches and in these cases results of the static analysis could be inaccurate.
This model was later updated to the final map shown in Figure 2. Static comparison between the expert-derived map and the final map from Figure 2 shows that these maps are similar. , some of the weights that have small magnitude in the final model are rounded to zero. The weights that have larger magnitude have the same sign between the two maps. We also observe that the weight developed by experts have lower precision. 07). Therefore, the static analysis of these two models gives very similar results.