scholarly journals Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System

2021 ◽  
Vol 11 (21) ◽  
pp. 9936
Author(s):  
Yunhui Luo ◽  
Xingguang Wang ◽  
Qing Wang ◽  
Yehong Chen
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Good Practice ◽  
Color Constancy ◽  
Image Features ◽  
Combination Strategy ◽  
Illuminant Estimation ◽  
Inference System ◽  
Benchmark Datasets ◽  
Inference Systems

Computational color constancy (CCC) is a fundamental prerequisite for many computer vision tasks. The key of CCC is to estimate illuminant color so that the image of a scene under varying illumination can be normalized to an image under the canonical illumination. As a type of solution, combination algorithms generally try to reach better illuminant estimation by weighting other unitary algorithms for a given image. However, due to the diversity of image features, applying the same weighting combination strategy to different images might result in unsound illuminant estimation. To address this problem, this study provides an effective option. A two-step strategy is first employed to cluster the training images, then for each cluster, ANFIS (adaptive neuro-network fuzzy inference system) models are effectively trained to map image features to illuminant color. While giving a test image, the fuzzy weights measuring what degrees the image belonging to each cluster are calculated, thus a reliable illuminant estimation will be obtained by weighting all ANFIS predictions. The proposed method allows illuminant estimation to be dynamic combinations of initial illumination estimates from some unitary algorithms, relying on the powerful learning and reasoning capabilities of ANFIS. Extensive experiments on typical benchmark datasets demonstrate the effectiveness of the proposed approach. In addition, although there is an initial observation that some learning-based methods outperform even the most carefully designed and tested combinations of statistical and fuzzy inference systems, the proposed method is good practice for illuminant estimation considering fuzzy inference eases to implement in imaging signal processors with if-then rules and low computation efforts.

scholarly journals Utilization of Current Data for Geospatial Analysis of the Appropriateness of Apple Plantation Land Based on Fuzzy Inference Systems

2019 ◽  
Vol 4 (1) ◽  
pp. 64
Author(s):  
Prayudi Lestantyo
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Geospatial Analysis ◽  
Current Data ◽  
Inference System ◽  
Apple Industry ◽  
Accuracy Test ◽  
Spatial Factor ◽  
Apple Production ◽  
Inference Systems

Apple is a high-value import fruit in Indonesia. One of the Apple production centers in Indonesia is Batu City, but the results tend to be declining in every year. To fulfill the demand of domestic apple industry, it is than a must to open new plantation land by observing the spatial factor. Expert and direct field review are needed to perform the analysis of land suitability, so that it will takes a lot of time and effort. Therefore, a smart system that can conduct geospatial analysis by using fuzzy inference system is developed. The data was obtained by using satellite imagery, data interpolation, and digitized and then analyzed into information. The analysis was performed on each pixel with six variable inputs including altitude, rainfall, humidity, air temperature, soil type and sun shine intensity. Besides that, the five-clustering output makes the results more accurate. From the results of the accuracy test, it is obtained a 92,86% accuracy, by comparing the results of the spatial analysis using fuzzy inference system with direct review on the field.

Fuzzy Logic-Based Inference Systems

2015 ◽  
pp. 209-239
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Representation ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Mathematical Formulation ◽  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Adaptive Networks ◽  
Inference System ◽  
Inference Systems

This chapter presents the mathematical formulation of the fuzzy logic-based inference systems, used as means to infer about the response of ill-conditioned systems, based on the field knowledge representation in the fuzzy world. Particular approaches are explored, e.g., Fuzzy Inference System (FIS), Adaptive Networks-based FIS (ANFIS), Intuitionistic FIS (IFIS) and Fuzzy Cognitive Map (FCM), surfacing their potentialities in modeling applications, such as those in the field of learning, examined in the chapters of Part III that follow.

Demand forecasting in the beauty industry using fuzzy inference systems

2020 ◽  
Vol 15 (4) ◽  
pp. 1389-1417
Author(s):  
Ricardo Felicio Souza ◽  
Peter Wanke ◽  
Henrique Correa
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Theory And Practice ◽  
Forecast Errors ◽  
Fuzzy Inference Systems ◽  
Inference System ◽  
Content Type ◽  
Forecasting Methods ◽  
Inference Systems

Purpose This study aims to analyze the performance of four different fuzzy inference system-based forecasting tools using a real case company. Design/methodology/approach The forecasting tools were tested using 27 products of the nail polish line of a multinational beauty company and the performance of said tools was compared to those of the company’s previous forecasting methods that were basically qualitative (informal and intuition-based). Findings The performance of the methods analyzed was compared by using mean absolute percentage error. It was possible to determine the characteristics and conditions that make each model the best for each situation. The main takeaways were that low kurtosis, negatively skewed demand time-series and longer horizon forecasts that favor the fuzzy inference system-based models. Besides, the results suggest that the fuzzy forecasting tools should be preferred for longer horizon forecasts over informal qualitative methods. Originality/value Notwithstanding the proposed hybrid modeling approach based on fuzzy inference systems, our research offers a relevant contribution to theory and practice by shedding light on the segmentation and selection of forecasting models, both in terms of time-series characteristics and forecasting horizon. The proposed fuzzy inference systems showed to be particularly useful not only when time-series distributions present no clear central tendency (that is, they are platykurtic or dispersed around a large plateau around the median, which is the characteristic of negative kurtosis), but also when mode values are greater than median values, which in turn are greater than mean values. This large tail to the left (negative skewness) is typical of successful products whose sales are ramping up in early stages of their life cycle. For these, fuzzy inference systems may help managers screen out forecast bias and, therefore, lower forecast errors. This behavior also occurs when managers deal with forecasts of longer horizons. The results suggest that further research on fuzzy inference systems hybrid approaches for forecasting should emphasize short-term forecasting by trying to better capture the “tribal” managerial knowledge instead of focusing on less dispersed and slower moving products, where the purely qualitative forecasting methods used by managers tend to perform better in terms of their accuracy.

Multilayer Optimization Approach for Fuzzy Systems

2011 ◽  
pp. 1121-1129
Author(s):  
Ivan N. Silva ◽  
Rogerio A. Flauzino
Keyword(s):  
Energy Function ◽  
Fuzzy Inference System ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Energy Functions ◽  
Inference System ◽  
Squared Error ◽  
The Mean ◽  
Inference Systems

The design of fuzzy inference systems comes along with several decisions taken by the designers since is necessary to determine, in a coherent way, the number of membership functions for the inputs and outputs, and also the specification of the fuzzy rules set of the system, besides defining the strategies of rules aggregation and defuzzification of output sets. The need to develop systematic procedures to assist the designers has been wide because the trial and error technique is the unique often available (Figueiredo & Gomide, 1997). In general terms, for applications involving system identification and fuzzy modeling, it is convenient to use energy functions that express the error between the desired results and those provided by the fuzzy system. An example is the use of the mean squared error or normalized mean squared error as energy functions. In the context of systems identification, besides the mean squared error, data regularization indicators can be added to the energy function in order to improve the system response in presence of noises (from training data) (Guillaume, 2001). In the absence of a tuning set, such as happens in parameters adjustment of a process controller, the energy function can be defined by functions that consider the desired requirements of a particular design (Wan, Hirasawa, Hu & Murata, 2001), i.e., maximum overshoot signal, setting time, rise time, undamped natural frequency, etc. From this point of view, this article presents a new methodology based on error backpropagation for the adjustment of fuzzy inference systems, which can be then designed as a three layers model. Each one of these layers represents the tasks performed by the fuzzy inference system such as fuzzification, fuzzy rules inference and defuzzification. The adjustment procedure proposed in this article is performed through the adaptation of its free parameters, from each one of these layers, in order to minimize the energy function previously specified. In principle, the adjustment can be made layer by layer separately. The operational differences associated with each layer, where the parameters adjustment of a layer does not influence the performance of other, allow single adjustment of each layer. Thus, the routine of fuzzy inference system tuning acquires a larger flexibility when compared to the training process used in artificial neural networks. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, such methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno.

scholarly journals CONTENT-BASED IMAGE RETRIEVAL USING EXPRESSION SENSITIVITY BY FUZZY INFERENCE SYSTEM

Kursor ◽  
2016 ◽  
Vol 8 (1) ◽  
pp. 21
Author(s):  
Sukmawati Nur Endah
Keyword(s):  
Image Retrieval ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Image Size ◽  
Image Region ◽  
Inference System ◽  
Content Base Image Retrieval ◽  
Text Query

Image retrieval can be divided into two types context-based and the content-based. Image retrieval based on the content refers to the image features such as color, texture, shape, semantics or sensations. This paper addresses the content-base image retrieval system based on expression sensitivity. It can be image or text query for input the system. Based on Itten theory, expression sensitivity consist of warm, cold, relax, anxious, and life. The research system uses two fuzzy inference system. Firstly, fuzzy inference system is used to decide image region of color. The image size is 256 x 256 pixel. Output the first fuzzy inference system is input for the second fuzzy inference system. The second fuzzy inference system is used to determined expression sensitivity of image. Degree of accuracy based on respondent from 50 images and 20 respondents is 42% for text query and 55% for image query. The further research, it can be used for other image such as medical image with certain criteria.

scholarly journals Comparative Study of Mamdani-type and Sugeno-type Fuzzy Inference Systems for Coupled Water Tank

2020 ◽  
Vol 3 (1) ◽  
pp. 42
Author(s):  
Halim Mudia
Keyword(s):  
Fuzzy Inference System ◽  
Chemical Engineering ◽  
Fuzzy Inference ◽  
Engineering System ◽  
Water Tank ◽  
Fuzzy Inference Systems ◽  
Process Industries ◽  
Inference System ◽  
Inference Systems ◽  
Level 2

The level and flow control in tanks are the heart of all chemical engineering system. The control of liquid level in tanks and flow between tanks is a basic problem in the process industries. Many times the liquids will be processed by chemical or mixing treatment in the tanks, but always the level of fluid in the tanks must be controlled and the flow between tanks must be regulated in presence of non-linearity. Threfore, in this paper will use fuzzy inference systems to control of  level 2 are developed using Mamdani-type and Sugeno-type fuzzy models. The outcome obtained by two fuzzy inference systems is evaluated. This paper summarizes the essential variation among the Mamdani-type and Sugeno-type fuzzy inference systems with setpoint of level is 10 centimeter. Matlab fuzzy logic toolbox is used for the simulation of both the models. This also confirms which one is a superior choice of the two fuzzy inference systems to control of level 2 in tank 2. The results show madani-type fuzzy inference system is superior as compared to sugeno-type fuzzy inference system.

RULE CORRELATION AND CHOQUET INTEGRATION IN FUZZY INFERENCE SYSTEMS

2008 ◽  
Vol 16 (05) ◽  
pp. 601-626 ◽  
Author(s):  
R. A. MARQUES PEREIRA ◽  
R. A. RIBEIRO ◽  
P. SERRA
Keyword(s):  
Fuzzy Inference System ◽  
Correlation Matrix ◽  
Fuzzy Inference ◽  
Integration Scheme ◽  
Rule Base ◽  
Inference System ◽  
Aggregation Scheme ◽  
Inference Systems ◽  
Rule Bases ◽  
Takagi Sugeno

We propose an extension of the Takagi-Sugeno-Kang (TSK) fuzzy inference system, using Choquet integration for aggregating the single rule outputs. In the new Choquet-TSK fuzzy inference system, the pairwise synergies between rules are encoded in a rule correlation matrix computed from the activation pattern of the rule base. The rule correlation matrix is then used to modulate the parameters of the Choquet integration scheme in order to compensate for the effect of rule synergies, which are present in most rule bases to a higher or lesser extent.The standard TSK fuzzy inference system remains a particular instance of the proposed Choquet-TSK extension and corresponds to the ideal case of rule independence. However, when rule correlation is present, the Choquet-TSK fuzzy inference system takes it into account when computing the final output of the system. On the basis of the rule correlation matrix, the new aggregation scheme of the Choquet-TSK fuzzy inference system attenuates the effective weight of positively correlated rules and emphasizes that of negatively correlated rules. Some case studies are discussed in order to illustrate the proposed methodology.

scholarly journals Application of a Fuzzy Inference System for Optimization of an Amplifier Design

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2168
Author(s):  
M. Isabel Dieste-Velasco
Keyword(s):  
Monte Carlo ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Electronic Circuits ◽  
Fuzzy Inference Systems ◽  
Inference System ◽  
Amplifier Design ◽  
Circuit Components ◽  
Inference Systems ◽  
Response Variables

Simulation programs are widely used in the design of analog electronic circuits to analyze their behavior and to predict the response of a circuit to variations in the circuit components. A fuzzy inference system (FIS) in combination with these simulation tools can be applied to identify both the main and interaction effects of circuit parameters on the response variables, which can help to optimize them. This paper describes an application of fuzzy inference systems to modeling the behavior of analog electronic circuits for further optimization. First, a Monte Carlo analysis, generated from the tolerances of the circuit components, is performed. Once the Monte Carlo results are obtained for each of the response variables, the fuzzy inference systems are generated and then optimized using a particle swarm optimization (PSO) algorithm. These fuzzy inference systems are used to determine the influence of the circuit components on the response variables and to select them to optimize the amplifier design. The methodology proposed in this study can be used as the basis for optimizing the design of similar analog electronic circuits.

Image Spam Detection Scheme Based on Fuzzy Inference System

2017 ◽  
pp. 147-165
Keyword(s):  
Principal Components ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Principal Component ◽  
Image Features ◽  
Spam Detection ◽  
Inference System ◽  
Detection Scheme ◽  
Image Spam ◽  
Statistical Noise

The evasion techniques used by image spam impose new challenges for e-mail spam filters. Effectual image spam detection requires selection of discriminative image features and suitable classification scheme. Existing research on image spam detection utilizes only visual features such as color, appearance, shape and texture, while no efforts is made to employ statistical noise features. Further, most image spam classification schemes assume existence of clear cut demarcation between extracted features from genuine image and image spam dataset. In this chapter, we attempt to solve these issues; by proposing a novel server side solution called F-ISDS (Fuzzy Inference System based Image Spam Detection Scheme). F-ISDS considers statistical noise features along with the standard image features and meta-data features. F-ISDS employs dimensionality reduction using Principal Component Analysis (PCA) to map selected set of n features into a set of m principal components. Based on the selected significant principal components, input/output membership functions and rules are designed for Fuzzy Inference System (FIS) classifier. FIS provides a computationally simple and an intuitive means of performing the image spam detection. Email server can tag email with this knowledge so that client can take decision as per the local policy. Further, a Linear Regression Analysis is used to model the relationship between selected principal components and extracted features for classification phase. Experimental results confirm the efficacy of the proposed solution.

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