An Approach for Online Weight Update Using Particle Swarm Optimization in Dynamic Fuzzy Cognitive Maps

Fuzzy cognitive maps (FCM) is a method to update a given initial vector to obtain the most stable state of a system, using a neighborhood of weights between these vectors and updating it over a series of iterations. FCMs are modeled with graphs. Neighbor weights between nodes are between-1 and 1. Nowadays it is used in business management, information technology, communication, health and medical decision making, engineering and computer vision. In this study, a dynamic FCM structure based on Particle Swarm Optimization (PSO) is given for determining node weights and online updating for modeling of dynamic systems with FCMs. Neighborhood weights in dynamic FCMs can be updated instantly and the system feedback is used for this update. In this work, updating the weights of the dynamic FCM is a PSO based approach that takes advantage of system feedback. In previous literature suggestions, dynamic FCM structure performs the weight updating process by using rule-based methods such as Hebbian. Metaheuristic methods are less complex and more efficient than rule-based methods in such optimization problems. In the developed PSO approach, the initialize vector state of the system, the weights between the vector nodes, and the desired steady state vector are taken into consideration. As a fitness function, the system has benefited from the convergence state to the desired steady state vector. As a stopping criterion for PSO, 100 ∗ n number of iteration limits have been applied for the initial vector with n nodes. The proposed method has been tested for five different scenarios with different node counts. © 2018 IEEE

Multiple Object Tracking with Dynamic Fuzzy Cognitive Maps Using Deep Learning

Object tracking is the process of matching objects detected on image sequences onto image frames. There are different types of object tracking applications used for different scenarios. For example, if a single object is being traced on an image, this is a single object tracking application. Tracking multiple objects on an image is called multiple object tracking. Fuzzy cognitive maps, on the other hand, form the model of a system by using the features of a system and the relationships between these features. Here, the single object tracking process is a matching problem, so FCM assumes a classifier role. In conventional operations, FCMs use the same weight matrix for all initial concept values. This can reduce the performance of the solution that the FCM produces for the problem it tackles. The FCM structure we use here takes advantage of the dynamic learning of FCM weights with deep learning. The study was tested on different image sequences and the performance of the proposed method were very satisfactory. © 2019 IEEE