13 research outputs found
Optimal Fuzzy Model Construction with Statistical Information using Genetic Algorithm
Fuzzy rule based models have a capability to approximate any continuous
function to any degree of accuracy on a compact domain. The majority of FLC
design process relies on heuristic knowledge of experience operators. In order
to make the design process automatic we present a genetic approach to learn
fuzzy rules as well as membership function parameters. Moreover, several
statistical information criteria such as the Akaike information criterion
(AIC), the Bhansali-Downham information criterion (BDIC), and the
Schwarz-Rissanen information criterion (SRIC) are used to construct optimal
fuzzy models by reducing fuzzy rules. A genetic scheme is used to design
Takagi-Sugeno-Kang (TSK) model for identification of the antecedent rule
parameters and the identification of the consequent parameters. Computer
simulations are presented confirming the performance of the constructed fuzzy
logic controller
Genetic programming and bacterial algorithm for neural networks and fuzzy systems design
In the field of control systems it is common to use techniques based on model
adaptation to carry out control for plants for which mathematical analysis may be
intricate. Increasing interest in biologically inspired learning algorithms for control
techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this
line, this paper gives a perspective on the quality of results given by two different
biologically connected learning algorithms for the design of B-spline neural networks
(BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP)
for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for
fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the
GP algorithm is outlined, enabling the designer to obtain models more adequate for
their intended use
Modeling integrated sustainable waste management systems by fuzzy cognitive maps and the system of systems concept
This paper describes the problems relating to the complexity of modern waste management systems. We present a new approach to selecting a better waste management solution. For a large and complex system it is extremely difficult to describe the entire system by a precise mathematical model. Therefore, we propose the use of Fuzzy Cognitive Maps (FCM), its combination with the Bacterial Evolutionary Algorithm (BEA) and the system of systems approach to support the planning and decision making process of integrated systems
Effect of the initial population construction on the DBMEA algorithm searching for the optimal solution of the traveling salesman problem
There are many factors that affect the performance of the evolutionary and memetic algorithms. One of these factors is the proper selection of the initial population, as it represents a very important criterion contributing to the convergence speed. Selecting a conveniently preprocessed initial population definitely increases the convergence speed and thus accelerates the probability of steering the search towards better regions in the search space, hence, avoiding premature convergence towards a local optimum. In this paper, we propose a new method for generating the initial individual candidate solution called Circle Group Heuristic (CGH) for Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), which is built with aid of a simple Genetic Algorithm (GA). CGH has been tested for several benchmark reference data of the Travelling Salesman Problem (TSP). The practical results show that CGH gives better tours compared with other well-known heuristic tour construction methods
Parameter Optimization of Deep Learning Models by Evolutionary Algorithms
Deep learning is a very popular gradient based search technique nowadays. In this field of machine learning
we usually apply neural networks with various structure. The algorithms of the deep learning techniques and the structure of the applied networks have several parameters that have a huge impact on the performance of the search technique. These parameters are called hyperparameters. The aim of our current research is to optimize these hyperparameters using evolutionary and swarm based optimization algorithms
Identification of the initial rule-base of a multi-stroke fuzzy-based character recognition method with meta-heuristic techniques
This paper summarizes the basic concept of the designed a fuzzy-based character recognition algorithm family and the results of the optimization of its rule-base with two various meta-heuristic methods, the Imperialist Competitive Algorithm and the bacterial evolutionary algorithm. The results are presented and compared with two other methods from literature after a short overview of the recognition algorithm
Bead geometry modeling on uneven base metal surface by fuzzy systems for multi-pass welding
This paper presents a modeling method of weld bead profiles deposited on uneven base metal surfaces and its application in multi-pass welding. The robotized multi-pass tungsten inert gas welding requires precise positioning of the weld beads to avoid welding defects and achieve the desirable welding join since the weld bead shapes depend on the surface of the previously deposited beads. The proposed model consists of fuzzy systems to estimate the coefficients of the profile function. The characteristic points of the trapezoidal membership functions in the rule bases are tuned by the Bacterial Memetic Algorithm during supervised training. The fuzzy systems are structured as multiple-input-single-output systems, where the inputs are the welding process variables and the coefficients of the shape functions of the segments underlying the modeled bead; the outputs are the coefficients of the bead shape function. Each segment surface is approximated by a second-order polynomial function defined in the weld bead鈥檚 local coordinate system. The model is developed from empirical data collected from single and multi-pass welding. The performance of the proposed model is compared with a
multiple linear regression model. During the experimental validation, first, the individual beads are evaluated by
comparing the estimated coefficients of the profile function and other bead characteristics (bead area, width,
contact angles, and position of the toe points) with the measurements, and the estimations of a multiple linear
regression model. Second, the sequential placement of the weld beads is evaluated while filling a straight Vgroove
by comparing the estimated bead characteristics with the measurements and calculating the accumulated
error of the filled groove cross-section. The results show that the proposed model provides a good estimation of
the bead shapes during deposition on uneven base metal surfaces and outperforms the regression model with low
error in both validation cases. Furthermore, it is experimentally validated that the derived bead characteristics provide a suitable measure to identify locations sensitive to welding defects