Petros P. Groumpos

Modeling complex systems using fuzzy cognitive maps

This research deals with the soft computing methodology of fuzzy cognitive map (FCM). Here a mathematical description of FCM is presented and a new methodology based on fuzzy logic techniques for developing the FCM is examined. The capability and usefulness of FCM in modeling complex systems and the application of FCM to modeling and describing the behavior of a heat exchanger system is presented. The applicability of FCM to model the supervisor of complex systems is discussed and the FCM-supervisor for evaluating the performance of a system is constructed; simulation results are presented and discussed.

Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization

This paper introduces a new learning algorithm for Fuzzy Cognitive Maps, which is based on the application of a swarm intelligence algorithm, namely Particle Swarm Optimization. The proposed approach is applied to detect weight matrices that lead the Fuzzy Cognitive Map to desired steady states, thereby refining the initial weight approximation provided by the experts. This is performed through the minimization of a properly defined objective function. This novel method overcomes some deficiencies of other learning algorithms and, thus, improves the efficiency and robustness of Fuzzy Cognitive Maps. The operation of the new method is illustrated on an industrial process control problem, and the obtained simulation results support the claim that it is robust and efficient.

Enhanced Learning in Fuzzy Simulation Models Using Memetic Particle Swarm Optimization

Fuzzy cognitive maps constitute an important simulation methodology that combines neural networks and fuzzy logic. The Fuzzy cognitive maps designed by the experts can be enhanced significantly through learning algorithms, which proved to increase their efficiency and accuracy of simulation. Recently, learning algorithms that employ particle swarm optimization for the minimization of properly defined objective functions have been introduced. In this work, we enhance these learning schemes by incorporating local search in PSO, resulting in a memetic particle swarm optimization learning algorithm. Three variants of the memetic algorithm are applied successfully for the optimization of an Ecological Industrial Park simulation system and they are compared also with the established particle swarm optimization learning schemes. Results are reported and discussed, deriving useful conclusions

Two-stage learning algorithm for fuzzy cognitive maps

A two-stage learning algorithm based on Hebbian learning rule and evolutionary computation technique is presented in this paper for training Fuzzy Cognitive Maps. Fuzzy Cognitive Maps is a soft computing technique for modeling complex systems, which combines the synergistic theories of neural networks and fuzzy logic. The methodology of developing Fuzzy Cognitive Maps (FCMs) relies on human expert experience and knowledge, but still exhibits weaknesses in utilization of learning methods. We investigate in this work a coupling of Differential Evolution algorithm and Unsupervised Hebbian learning algorithm, using both the global search capabilities of Evolutionary techniques and the effectiveness of the Nonlinear Hebbian learning rule. The proposed algorithm applied successfully in a real-world process control problem. Experimental results suggest that the two-stage learning strategy is capable to train FCMs effectively leading the system to desired steady states and determining the appropriate weight matrix.

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets

BACKGROUND AND OBJECTIVE In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. METHODS Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. RESULTS Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). CONCLUSION The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller.

Decision making in external beam radiation therapy based on fuzzy cognitive maps

This work introduces the use of the soft computing technique of Fuzzy Cognitive Maps to model the decision-making process of radiation therapy and develop an advanced system to estimate the delivered dose to the target volume. During radiotherapy planning numerous factors are taking into consideration that increase the complexity of the decision-making problem. The modeling methodology of FCM has the ability to integrate and consider different, discipline and conflicting factors to determine the dose. A Fuzzy Cognitive Map Model is developed, that can handle imprecise and uncertain information and is used as the decision-making model determining the radiation dose and the complex radiation therapy system. The proposed FCM model is implemented for a practical radiotherapy treatment planning case of gynecological cancer.

The challenge of using unsupervised learning algorithms for fuzzy cognitive maps

Fuzzy cognitive maps is a hybrid method based on fuzzy systems and neural networks and belonging in soft computing. The methodology of developing fuzzy cognitive maps (FCMs) is easily adaptable and relies on human expert experience and knowledge, but it exhibits weaknesses in utilization of learning methods. The external intervention (typically from experts) for the determination of FCM parameters and the convergence to undesired steady states are significant FCM deficiencies. Thus, it is necessary to overcome these deficiencies in order to improve efficiency and robustness of FCM. Weight adaptation methods can alleviate these problems by allowing the creation of less error prone FCMs where causal links-weights are adjusted through a learning process.

Mainstreaming innovation policy in less favoured regions: the case of Patras Science Park, Greece

Creativity and human capital are increasingly being recognised by an expanding body of work on regional economics, and policy and innovative workspaces. A short review of this literature provides the theoretical base for discussing a number of challenges related to mainstreaming creativity in regional and urban economies. Implementing innovation policies in peripheral, less favoured contexts is challenging and requires specific adaptations. This paper argues that a science park and triple-helix institutions can act to animate regional creativity in Europes less favoured regions. It illustrates this point with a case study of the regional economic and policy environment for innovation, creativity and entrepreneurship, in Patras, Greece. Lessons learnt include: the need for consistency and continuity in planning, local ownership of the initiatives, multilevel collaboration in the governance and effective collective learning channels and processes between academia, business and state government. Copyright , Beech Tree Publishing.

A comperative study using the methods of simulated annealing and Non-linear Hebbian Learning for Fuzzy Cognitive Maps performances

The challenging problem of modelling complex dynamic systems with uncertain environment is reviewed. The theory of Fuzzy Cognitive Maps (FCM), used in recent years to model a good number of systems and applications, is briefly presented. Various methods of FCM learning are presented, specifically the Simulated Annealing (SA) and the Non-linear Hebbian Learning (NHL) methods are analysed in detail. A comperative study between the two methods is performed and presented. The application of SA and NHL on an energy based FCM, leads to interesting results and is motivating for further research and improvement.

Hybrid model based on Decision Trees and Fuzzy Cognitive Maps for Medical Decision Support System

For medical decision making processes (diagnosing, classification, etc.) all decisions must be made effectively and reliably. Conceptual decision making models with the potential of learning capabilities are more appropriate and suitable for performing such hard tasks. Decision trees are a well known technique, which has been applied in many medical systems to support decisions based on a set of instances. On the other hand, the soft computing technique of Fuzzy Cognitive Maps (FCMs) is an effective decision making technique, which provides high performance with a conceptual representation of gathered knowledge and existing experience. FCMs have been used for medical decision making with emphasis in radiotherapy and classification tasks for bladder tumour grading. This paper proposes and presents an hybrid model derived from the combination and the synergistic application of the above mentioned techniques. The proposed Decision Tree-Fuzzy Cognitive Map model has enhanced operation and effectiveness based on both methods giving better accuracy results in medical decision tasks.