Fuzzy algorithm

The amount of cane fiber carried by cane carrier in a sugar mill varies due to non-uniformity of cane supply. The continuous variation of cane in Donnelly chute during the cane juice extraction inversely affects the cane juice extraction efficiency of mill. This paper deals with the development of algorithm for a three input fuzzy controller with an aim to maintain the cane level in Donnelly chute during cane juice extraction. The developed controller generates the required rake carrier motor speed in rpm depending upon the value of cane level in Donnelly chute, quantity of cane on rake carrier and the roll speed. The three inputs fuzzy controller is developed and simulated for six cases by using fuzzy logic toolbox of ‘MATLAB® version 7.11.0.584 (R2020b).

The impact of soft computing is increasing because of it's tolerant of imprecision, uncertainty, partial truth, and approximation nature. We can incorporate intelligence in industrial automation by using soft computing. Fuzzy knowledge based systems in its current form is influenced by Zadeh's 1965 paper on fuzzy sets. This paper reports a design methodology for developing a fuzzy inference system for controlling the level of cane in a sugar mill to give optimum juice extraction. The proposed system is designed by using two input parameters and one output parameter. For developing the system 'Fuzzy Logic' toolbox of 'MATLAB ® version 7.11.0.584 (R2020b) is used. Sugar mill parameters are taken from M/s Bagpat Cooperative Sugar Mill Limited, Bagpat (U.P).

Raw sugar is produced from the cane juice which is extracted from the series of five to six cane crushing mills. The uneven supply of cane during cane crushing affects juice extraction efficiency of the mill. Seventy to seventy five percent of the cane juice is extracted from the first mill and thus this mill plays an important role in overall efficiency of sugar production in a sugar mill. This research paper deals on the design methodology, implementation and functional verification of a conventional controller to maintain the cane level during cane crushing in Field Programmable Gate Array (FPGA). The VHDL model of proposed controller is developed using Xilinx ISE Design Suite 14.5.

The paper illustrates a new fuzzy approach for optimizing routing decisions. This illustration is being done, here, using packet switched networks. It implements the approach and analyzes the new coherent formats of link-state packets and routing table accrued from the implementation of approach. In this algorithm, each node in the network maintains a database containing values corresponding to linguistic variables described for the output mixed metric formed from delay and load. Therefore, describing the complete network topology more meticulously. Because the traffic in the network is quite variable, therefore, it is combined with delay to yield more efficacy in achieving improved routing decisions. Also, provides a fuzzy measure to quantify the effect of load on delay. Thus, the whole procedure is implemented over an example of packet switched network to analyze its working comprehensively.

Optimizing routing decisions has been an important subject in research of communication networks. Therefore, a new fuzzy mixed metric approach is introduced and the present paper proposes algorithms for the new fuzzy mixed metric approach, propounded to achieve improved routing decisions. The fuzzy approach proposes a mixed metric combining delay and load. The fundamental aim of the approach is to analyze the effect of the variability of load on delay. In other words, the approach is providing a fuzzy measure to quantify the effect of load on delay. The algorithms are proposed to provide a logical view of approach. Also, a routing model is proposed to understand the proposed approach more clearly and concisely.

The Fuzzy c-Means (FCM) clustering models were used for the discrimination of organic compounds using piezoelectric chemical sensor array data of 14 analytes. Appropriate clusters are found by the sum of the weighted quadratic distances between data points and cluster prototypes. A priori known information can be integrated into the clustering algorithm by using constrained prototypes. A sensor array was built using piezoelectric quartz crystal sensors. Four AT-cut quartz crystals with 9 MHz fundamental frequencies were applied. Sensing materials were OV1, OV275, ASI50, and polyphenil-ether. The appropriate coating materials were found by a principal component analysis. The application of the fuzzy clustering method has been proved to be reliable way of identifying similar, pure organic compounds.

The present study aims at developing an intelligent system of automating data analysis and prediction embedded in a fuzzy logic algorithm (FAUSY) to capture the relationship between environmental variables and sap flow measurements (Granier method). Environmental thermal gradients often interfere with Granier sap flow measurements since this method uses heat as a tracer, thus introducing a bias in transpiration flux calculation. The FAUSY algorithm is applied to solve measurement problems and provides an approximate and yet effective way of finding the relationship between the environmental variables and the natural temperature gradient (NTG), which is too complex or too ill-defined for precise mathematical analysis. In the process, FAUSY extracts the relationships from a set of input-output environmental observations, thus general directions for algorithm-based machine learning in fuzzy systems are outlined. Through an iterative procedure, the algorithm plays with the learning or forecasting via a simulated model. After a series of error control iterations, the outcome of the algorithm may become highly refined and be able to evolve into a more formal structure of rules, facilitating the automation of Granier sap flow data analysis. The system presented herein simulates the occurrence of NTG with reasonable accuracy, with an average residual error of 2.53% for sap flux rate, when compared to data processing performed in the usual way. For practical applications, this is an acceptable margin of error given that FAUSY could correct NTG errors up to an average of 76% of the normal manual correction process. In this sense, FAUSY provides a powerful and flexible way of establishing the relationships between the environment and NTG occurrences.

In this work we begin by performing a Principal Component Analysis (PCA) on NDVI computed over clear-sky pixels from VEGETATION data. Two techniques (i.e. ISODATA and Fuzzy Clustering) are applied to the two retained Principal Components and used to produce a land cover map over Portugal. Quality of performed classifications is assessed based on a confusion matrix, using as reference the Corine Land Cover Map. Obtained results seem to be quite reasonable, namely in the case of forests and crops and show to be comparable with other classifications such as GLC2000.

Solar energy for cooling systems has been widely used to fulfill the growing air conditioning demand. The advantage of this approach is based on the fact that the need of air conditioning is usually well correlated to solar radiation. These kinds of plants can work in different operation modes resulting on a hybrid system. The control approaches designed for this kind of plant have usually a twofold goal: (a) regulating the outlet temperature of the solar collector field and (b) choosing the operation mode. Since the operation mode is defined by a set of valve positions (discrete variables), the overall control problem is a nonlinear optimization problem which involves discrete and continuous variables. This problems are difficult to solve within the normal sampling times for control purposes (around 20–30 s). In this paper, a two layer control strategy is proposed. The first layer is a nonlinear model predictive controller for regulating the outlet temperature of the solar field. T...

The fuzzy c-means (FCM) clustering models were used for the discrimination of organic compounds using piezoelectric chemical sensor array data of 14 analytes. Appropriate clusters are found by the sum of the weighted quadratic distances between data points and cluster prototypes. A priori known information can be integrated into the clustering algorithm by using constrained prototypes. A sensor array was built using piezoelectric quartz crystal sensors. Four AT-cut quartz crystals with 9 MHz fundamental frequencies were applied. Sensing materials were OV1, OV275, ASI50, and polyphenil-ether. The appropriate coating materials were found by principal component analysis. The application of the fuzzy clustering method has been proved to be a reliable way of identifying similar, pure organic compounds.