using image processing and artificial intelligence systems in agriculture and food industry is increasing daily. the purpose of this research is to study the feasibility of using image processing technique in predicting process of moisture content changes on green tea sheets during the drying using predictive artificial intelligence systems such as: artificial neural networks and adaptive neuro...

The authors propose an automated neuro-fuzzy system approach (with neural network subsystem) to power quality assessment incorporating equipment susceptibility patterns. The system is expected to handle dependencies between superposition of different disturbances and specific devices’ susceptibility to disturbances. Two neural network architectures were applied: a well known radial-basis neural...

Function Points is an important and well-accepted software size metric. However, it is absolutely essential to accurately calibrate Function Point (FP), whose aims are to fit specific software application, to reflect software industry trend, and to improve cost estimation. Neuro-Fuzzy is a technique that incorporates the learning ability from neural network and the ability to capture human know...

The Neuro-Symbolic Hybrid Systems (NSHS) are used to solve problems where there exists a necessity of combining and integrating the artificial neural networks and the symbolic representations in only one system in order to obtain better results. We developed a NSHS Methodology to integrate the knowledge of a human expert and the numeric knowledge obtained from a computer vision process. We impl...

Evolving recurrent neural networks for behavior control of robots equipped with larger sets of sensors and actuators is difficult due to the large search spaces that come with the larger number of input and output neurons. We propose constrained modularization as a novel technique to reduce the search space for such evolutions. Appropriate neural networks are divided manually into logically and...

Most processes in industry are characterized by nonlinear and time-varying behavior. Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neuro-fuzzy models are gradually becoming established not only in the academia but ...

This paper investigates the identification of nonlinear systems by utilizing soft-computing approaches. As the identification methods, Feedforward Neural Network architecture (FNN), Radial Basis Function Neural Networks (RBFNN), Runge-Kutta Neural Networks (RKNN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) based identification mechanisms are studied and their performances are comparative...

In this paper, an improved cascade control methodology for superheated processes is developed, in which the primary PID controller is implemented by neural networks trained by minimizing error entropy criterion. The entropy of the tracking error can be estimated recursively by utilizing receding horizon window technique. The measurable disturbances in superheated processes are input to the neur...

Fuzzy inference systems and neural networks are complementary technologies in the design of adaptive intelligent systems. Artificial Neural Network (ANN) learns from scratch by adjusting the interconnections between layers. Fuzzy Inference System (FIS) is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. A neuro-fuzzy system is sim...