Adaptive neuro-fuzzy classifier for evaluating the technology effectiveness based on the modified Wang and Mendel fuzzy neural production MIMO-network

The paper describes practical results gained during synthesis of the classification model for estimating technology effectiveness when solving multi-criteria problems of expert data analysis (Foresight) aimed to identify technological breakthroughs and strategic perspectives of scientific, technological and innovative developmen

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

The Use of Intelligent Systems for Planning and Scheduling of Product Development Projects

AbstractThe paper investigates the use of intelligent systems to identify the factors that significantly influence the duration of new product development. These factors are identified on the basis of an internal database of a production enterprise and further used to estimate the duration of phases in product development projects. In the paper, some models and methodologies of the knowledge discovery process are compared and a method of knowledge acquisition from an internal database is proposed. The presented approach is dedicated to industrial enterprises that develop modifications of previous products and are interested in obtaining more precise estimates for project planning and scheduling. The example contains four stages of the knowledge discovery process including data selection, data transformation, data mining, and interpretation of patterns. The example also presents a performance comparison of intelligent systems in the context of variable reduction and preprocessing. Among data mining techniques, artificial neural networks and the fuzzy neural system are chosen to seek relationships between the duration of project phase and other data stored in the information system of an enterprise

Development of Artificial Intelligence Algorithm for Smart Irrigation Using Internet of Things (IoT)

Artificial Intelligence (AI) is the most recent agricultural technology. In agriculture, water is used to irrigate the plants so that they can flourish. Due to the scarcity of water in most parts of the world, the watering process is one of the most significant and crucial procedures. To address this issue, a smart irrigation system based on the Internet of Things (IoT) was developed utilizing AI technology, an Arduino Uno microcontroller, and sensors. The objective of this study is to develop a modified ANFIS (Adaptive Neuro-Fuzzy Inference System) AI algorithm for improved automated irrigation system decision control and to reduce the computing complexity of ANFIS architectural layers. It also seeks to develop an integrated system for monitoring and managing irrigation to increase agricultural output using MANFIS and the Internet of Things. Lastly, is to determine the difference of algorithmic complexity between the conventional ANFIS and Modified ANFIS. As a result, architectural layers were reduced into 3 layers, INPUT, PROCESS and OUTPUT. It also waters the plant automatically and sends signals regarding smart irrigation system information such as the tank's water level, the plant's soil moisture content, and the trigging factor, or the quantity of water to be released which enables the farmers to monitor and manage its irrigation system using the IoT. The simulations were carried out using MATLAB software, a fuzzy logic controller is used to control the whole system by providing its input, rules and output. To determine the computational complexity of each method, the ANFIS and modified ANFIS were examined with 100 % success rate

Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network

AbstractIn this paper, the Artificial Neural Network (ANN) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are used to predict the shear strength of Reinforced Concrete (RC) beams, and the models are compared with American Concrete Institute (ACI) and Iranian Concrete Institute (ICI) empirical codes. The ANN model, with Multi-Layer Perceptron (MLP), using a Back-Propagation (BP) algorithm, is used to predict the shear strength of RC beams. Six important parameters are selected as input parameters including: concrete compressive strength, longitudinal reinforcement volume, shear span-to-depth ratio, transverse reinforcement, effective depth of the beam and beam width. The ANFIS model is also applied to a database and results are compared with the ANN model and empirical codes. The first-order Sugeno fuzzy is used because the consequent part of the Fuzzy Inference System (FIS) is linear and the parameters can be estimated by a simple least squares error method. Comparison between the models and the empirical formulas shows that the ANN model with the MLP/BP algorithm provides better prediction for shear strength. In adition, ANN and ANFIS models are more accurate than ICI and ACI empirical codes in prediction of RC beams shear strength

Case-based reasoning: concepts, features and soft computing

Here we first describe the concepts, components and features of CBR. The feasibility and merits of using CBR for problem solving is then explained. This is followed by a description of the relevance of soft computing tools to CBR. In particular, some of the tasks in the four REs, namely Retrieve, Reuse, Revise and Retain, of the CBR cycle that have relevance as prospective candidates for soft computing applications are explained

A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results