Session Program

Abstract - Plant disease experimental data have been shown to fit better with a crisp Gompertz model rather than a logistic model. A fuzzy approach based on Zadeh's Extension Principle, which leads to four systems of two parameter dependent autonomous differential equations, is applied to this subject. The solution is monitored from the initial fuzzy conditions through to the three different domains and two sub-domains. While results show properties of the crisp Gompertz model being kept and then lost, this is still an appropriate generalized way to deal with uncertainty in plant disease evolution.

Abstract - The main goal of the work presented in the paper is to introduce the use of fuzzy logic in the Grey Wolf Optimizer (GWO) algorithm, specifically for dynamic simultaneous adaptation of the key parameters, which are determinant in the performance of the metaheuristic. The proposed approach for this modification of GWO using fuzzy logic is presented. In addition, a brief comparison between the traditional GWO algorithm and the Grey Wolf Optimizer using fuzzy logic for dynamic adaptation of parameters is reported. This research shows the individually dynamic adjustment of two parameters and a proposal of how to simultaneously adjust both parameters and finally we present the performance of these methods when they are tested with a set of benchmark functions, showing the advantage of using the simultaneous adaptation of parameters.

Abstract - the main aim of this paper is to use fuzzy inference systems for controlling the relevant parameters within the equations of the FWA algorithm. In other words, parameters that are considered constant in the traditional FWA and are now made dynamic by using fuzzy logic. It is worth mentioning that we also made a modification to the algorithm with the goal of having a better performance and the modification was to change the stopping criteria of the algorithm. In the conventional fireworks algorithm (FWA) the stopping criteria is based on the function evaluations, including, some variations or modifications of the FWA they also manage in the same way. We propose stopping criteria based on the iterations with the goal of having a more precise control and consequently, and thus, power in better way to control the output variables in a Fuzzy Inference System (FIS) of Mamdani type, since the number of iterations is used as the input variable. To demonstrate the validity of this modification we tested the algorithm with 12 benchmark functions with good results, and we call the proposed algorithm as Iterative Fuzzy Fireworks Algorithm and we denoted as IFFWA.

Abstract - Facial emotion recognition (FER) is a critical task for both human-human (HHI) and human-computer interactions (HCI). In this paper, a brain-inspired neural basis computational model of FER is proposed based on emotional neural networks (ENN), fuzzy c-means (FCM) and genetic algorithms (GA). The proposed model can be applied in both HHI and HCI applications. In HHI, it can be used for improving communication skills, and in HCI it can be used in various treatment processes e.g. anxiety treatment, cancer radiation treatment and remote children/elderlies monitoring systems. The proposed model consists of main modules of emotional brain which recognize the facial emotions. In the experimental studies, the proposed model is examined on children's facial sad recognition as a case study. The results show that our model is valid and can be applied for various FER tasks.

Abstract - Puzzling about cerebellum is its wide range of functional involvement with its seemingly uniform structure. Late views based on connectivity with other parts of the brain implicate it, from traditional lower level role in balance and coordination of movements, up to cognition and emotional level. Cerebellar models are used in attempts to explain this functional involvement, with possible benefits in solving similar technical problems. Information processing in the input layer used by these models can easily reach biological and practical limits in number of neurons required, even though cerebellum hosts more than half of total number of neurons in the brain, most of them in the input layer. To relax these limits for some representative tasks, new model of information processing at input layer, named granule cell layer, is used. This paper presents information processing at the basic granule cell-Golgi cell (GrC- GoC) building block of the first cerebellar processing layer, and overall cerebellar processing becoming similar to fuzzy models of Adaptive-Network-Based Fuzzy Inference System (ANFIS) and CoActive Neuro-Fuzzy Inference Systems (CANFIS) types. Simulink spiking model illustrates functionality of GrC-GoC block. It generates as a result multidimensional receptive fields from traditional group of mossy fibers with population code that drive GrC, which are gain modulated by other input of GoC driven by second group of mossy fibers with rate code. Simulink behavioral model of a cerebellum with new structure is used in two robotics applications. Model shows wider sharing of processing resources, and generalization capabilities that it offers, with more biologically plausible learning.