A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation

The fast and accurate modelling of thermal errors in machining is an important aspect for the implementation of thermal error compensation. This paper presents a novel modelling approach for thermal error compensation on CNC machine tools. The method combines the Adaptive Neuro Fuzzy Inference System (ANFIS) and Grey system theory to predict thermal errors in machining. Instead of following a traditional approach, which utilises original data patterns to construct the ANFIS model, this paper proposes to exploit Accumulation Generation Operation (AGO) to simplify the modelling procedures. AGO, a basis of the Grey system theory, is used to uncover a development tendency so that the features and laws of integration hidden in the chaotic raw data can be sufficiently revealed. AGO properties make it easier for the proposed model to design and predict. According to the simulation results, the proposed model demonstrates stronger prediction power than standard ANFIS model only with minimum number of training samples

Modeling Under MATLAB by ANFIS of Three-Phase Tetrahedral Transformer Using in Microwave Generator for Three Magnetrons Per Phase

This work deals with the modeling of a new three-phase tetrahedral transformer of HV power supply, which feeds three magnetrons per phase. The design of this new power supply is composed of three single-phase with magnetic shunt transformers coupling in star; each one is size to feed voltage-doubling cells, thereby feeds a magnetron. In order to validate the functionality of this power supply, we simulate it under Matlab-Simulink environment. Thus, we modeled nonlinear inductance using a new approach of neuro-fuzzy (ANFIS); this method based on the interpolation of the curve B(H) of ferromagnetic material, the results obtained gives forms of both voltages and currents, which shows that they are in accordance with those of experimental tests, respecting the conditions recommended by the magnetron manufacture

Optimization of roundness error in deep hole drilling using cuckoo search algorithm

In the manufacturing industry, machining is a part of all manufacture in almost all metal products. Machining of holes is one of the most common processes in the manufacturing industries. Deep hole drilling, DHD is classified as a complex machining process .This study presents an optimization of machining parameters in DHD using Cuckoo Search algorithm, CS comprising feed rate (f), spindle speed (s), depth of hole (d) and Minimum Quantity Lubrication MQL, (m). The machining performance measured is roundness error, Re. The real experimentation was designed based on Design of Experiment, DoE which is two levels full factorial with an added centre point. The experimental results were used to develop the mathematical model using regression analysis that used in the optimization process. Analysis of variance (ANOVA) and Fisher‘s statistical test (F-test) are used to check the significant of the model developed. According to the results obtained by experimental the minimum value of Re is 0.0222μm and by CS is 0.0198μm. For the conclusion, it was found that CS is capable of giving the minimum value of Re as it outperformed the result from the experimental

Effect of 3 Key Factors on Average End to End Delay and Jitter in MANET

A mobile ad-hoc network (MANET) is a self-configuring infrastructure-less network of mobile devices connected by wireless links where each node or mobile device is independent to move in any desired direction and thus the links keep moving from one node to another. In such a network, the mobile nodes are equipped with CSMA/CA (carrier sense multiple access with collision avoidance) transceivers and communicate with each other via radio. In MANETs, routing is considered one of the most difficult and challenging tasks. Because of this, most studies on MANETs have focused on comparing protocols under varying network conditions. But to the best of our knowledge no one has studied the effect of other factors on network performance indicators like throughput, jitter and so on, revealing how much influence a particular factor or group of factors has on each network performance indicator. Thus, in this study the effects of three key factors, i.e. routing protocol, packet size and DSSS rate, were evaluated on key network performance metrics, i.e. average delay and average jitter, as these parameters are crucial for network performance and directly affect the buffering requirements for all video devices and downstream networks

Solar Tracking System based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Fotovoltaik panellerin güç toplama verimliliğini artırmak için genellikle güneş takip sistemleri (GTS) ile entegre edilmelidir. Bu çalışmada, uyarlamalı sinirsel bulanık çıkarım uygulaması ile GTS sunulmuştur. GTS, zenit ve azimut açılarını kontrol eden iki motora sahip çift eksenli olarak tasarlanmıştır. Bu motorların hızının kontrol edilmesi için ANFIS’in tasarlanmasından sonra bulanık mantık kontrolörünün giriş-çıkış ilişkisini öğrenmek için yapay sinir ağı eğitilmiştir. Pozisyon hatası ve hatanın değişimi modellerin girişi olarak alınmıştır. Motora uygulanan gerilim modellerin çıkışı olarak alınmıştır. ANFIS modelde, deneysel verilerden doğrudan üretilen kurallar kümesine sahip yapay sinir ağının öğrenme yeteneği ile bulanık çıkarım modeli birleştirilir. Sonuç olarak, elde edilen sonuçlar GTS için amaçlanan kontrol yaklaşımının doğru cevap ve takip etme etkinliğini doğrular.Solar tracking systems (STS) should usually be integrated with photovoltaic (PV) panel so that the photovoltaic panels can increase power collection efficiency. In this paper, STS with implementation of adaptive neuro-fuzzy inference system (ANFIS) is presented. STS designed as dual axis has two motors that control azimuth angle and zenith angle. After designing an ANFIS for controlling these motors' speed, a Neural Network is trained to learn the input–output relationship of fuzzy logic controller. Position error and error variation were taken as model’s inputs. Applied voltage to the motor was taken as model's output. The ANFIS model is combined modeling function of fuzzy inference with the learning ability of artificial neural network that has set of rules generated directly from the experimental data. Finally, the obtained results confirm the tracking efficiency and correct response of the proposed control approach for STS

Returned Product Acquisition Pricing by Adaptive Neuro Fuzzy Inference System

In recent years, reverse logistics have become increasingly important for the firms as a both environmental and economical approach. By collecting the returned products, firms realize to recover after kind of activities. In return products collection, due to the fact that each returned products have different functionality, determining the acquisition price of the used products is an important problem. For this reason, a pricing approach that can be used for collecting returned products is proposed in this study. Since the different product models can be exist and the acquisition price can be affected by the new product price, the acquisition price is predicted by the ratio of the new product price to acquisition price. In this study, the acquisition price ratio to new product price is modeled by the adaptive neuro fuzzy inference system and a case study is conducted for the used cell phones collection. Four phone models that have different release dates take into consideration with general appearance and functionality parameters. When the results are examined, the proposed method prediction's is pretty close to the expert view

Spatial Prediction of Slope Failures in Support of Forestry Operations Safety

This study produces a slope failure susceptibility map for evaluation of the Caspian Forest for its capacity to support road construction and timber harvesting. Fifteen data layers were used as slope failure conditioning factors, and an inventory map of recent failures was used to detect the most susceptible areas. Five different datasets of conditioning factors were constructed to compare the efficiency of one over the other in susceptibility assessment. Slope failure susceptibility maps were produced using an adaptive neuro-fuzzy interface system (ANFIS) and geographical information system (GIS). The accuracy of the maps was then evaluated by the area under curve (AUC). The validation results suggest that the ANFIS model with input conditioning factors of slope degree, slope aspect, altitude, and lithology performed the best (AUC=83.74%) among the various ANFIS models explored here. The five ANFIS models have performed reasonably well, and the maps allow development of prudent hazard mitigation plans for the safety in forestry operations

Prilagodljivi neuro-fazi model za predviđanje tehnoloških parametara

The main goal of each technologist is the prediction of technological parameters by fulfilling the set design and technological demands. The work of the technologist is made easier by acquired knowledge and previous experience. A plan of input-output data was made by using the hybrid system of modelling ANFIS (Adaptive Neuro-Fuzzy Inference System) based on the results of seam tube production. This plan is the prerequisite for generating the system of fuzzy logic. The generated system can be used to estimate the output (speed of polishing) based on the given input (external tube diameter, oval shaping of the tube after the first phase of production, gradation of belts for grinding or polishing, condition of belts - time of usage, pressure of belts).The more precise predictions of technological time provided by the model supplement the previously defined manufacturing operations, replace the predictions based on the technologists\u27 experience and form the basis on which to plan production and control delivery times. The work of technologists is thus made easier and the production preparation technological time shorter.Procijeniti tehnološke parametre na način da se ispune postavljeni konstrukcijski i tehnološki zahtjevi cilj je i želja svakog tehnologa. Procjenu tehnologu mogu olakšati prikupljena znanja i ranije stečena iskustva. Na temelju sustavno prikupljenih podataka iz proizvodnje šavnih cijevi u radu je primjenom hibridnog sustava za modeliranje ANFIS (Adaptive Neuro-Fuzzy Inference System) oblikovan plan ulazno/izlaznih podataka. Taj je plan pretpostavka za generiranje sustava neizrazitog zaključivanja. Generirani sustav ima mogućnost procjene izlaza (brzine poliranja) na temelju danih ulaza (vanjski promjer cijevi, ovalnost cijevi nakon prve faze proizvodnje, gradacija remenja za brušenje ili poliranje, stanje remenja - vrijeme uporabe remenja, pritisak remenja). Točnije procjene tehnološkog vremena koje daje model upotpunjavaju prethodno definirane tehnološke operacije, zamjenjuje iskustvene procjene tehnologa i predstavljaju osnovu za planiranje proizvodnje i kontrolu rokova isporuke. Na ovaj se način olakšava rad tehnologa i skraćuje vrijeme tehnološke pripreme proizvodnje

Adaptive Cooperative Learning Methodology for Oil Spillage Pattern Clustering and Prediction

The serious environmental, economic and social consequences of oil spillages could devastate any nation of the world. Notable aftermath of this effect include loss of (or serious threat to) lives, huge financial losses, and colossal damage to the ecosystem. Hence, understanding the pattern and  making precise predictions in real time is required (as opposed to existing rough and discrete prediction) to give decision makers a more realistic picture of environment. This paper seeks to address this problem by exploiting oil spillage features with sets of collected data of oil spillage scenarios. The proposed system integrates three state-of-the-art tools: self organizing maps, (SOM), ensembles of deep neural network (k-DNN) and adaptive neuro-fuzzy inference system (ANFIS). It begins with unsupervised learning using SOM, where four natural clusters were discovered and used in making the data suitable for classification and prediction (supervised learning) by ensembles of k-DNN and ANFIS. Results obtained showed the significant classification and prediction improvements, which is largely attributed to the hybrid learning approach, ensemble learning and cognitive reasoning capabilities. However, optimization of k-DNN structure and weights would be needed for speed enhancement. The system would provide a means of understanding the nature, type and severity of oil spillages thereby facilitating a rapid response to impending oils spillages. Keywords: SOM, ANFIS, Fuzzy Logic, Neural Network, Oil Spillage, Ensemble Learnin