scholarly journals Edge Detection of Different Images using Soft Computing Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 222-226
Keyword(s):  
Soft Computing ◽  
Ant Colony Algorithm ◽  
Fuzzy Inference ◽  
Signal To Noise Ratio ◽  
Standard Test ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Segmentation Methods ◽  
Soft Computing Techniques ◽  
Fuzzy Genetic

The technique by which an image or photograph is divided into several number of parts in order to analyze the segmented components such as colors, textures grey scale and edges/boundaries of the entities which are present in the image is called as image segmentations. Images obtained by segmentation methods are more understandable as compared to the original images. In the digital snap shot segmentation is essentially used to detect object boundaries present in the image. The paper presents the comparative analysis of image segmentation using soft computing methods.In this paper, we included genetic algorithm, ant colony algorithm, neural network, neuro-fuzzy genetic and adaptive neuro-fuzzy inference system. The techniques are tested on six standard test images. The peak signal to noise ratio (PSNR)is calculated for GA and ACO techniques. The results which are obtained by the above techniques prove that the value of PSNR for GA is much more as compared to the ACO technique

scholarly journals NEURO-FUZZY APPROACH TO PREDICTIVE MODELING OF EMISSIONS FROM BIODIESEL POWERED TRANSIT BUSES

Transport ◽  
2012 ◽  
Vol 26 (4) ◽  
pp. 334-352 ◽  
Author(s):  
Kasthurirangan Gopalakrishnan ◽  
Abhisek Mudgal ◽  
Shauna Hallmark
Keyword(s):  
Soft Computing ◽  
Real World ◽  
Fuzzy Inference System ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Inference System ◽  
Measuring Device ◽  
Transit Buses ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

The rise in freight passenger transportation is responsible for air pollution, green house gas emissions (especially CO2) and high fuel demand. New engine technology and fuels are discovered and tested throughout the world. Biodiesel, an alternative for diesel, has been seen as a solution. However, the amount of emissions generated by a biodiesel fueled vehicle has not been understood well since most research studies of this kind reported in the literature were conducted in the laboratory. In the present study, emissions (NOx, HC, CO, CO2 and PM) were measured from biodiesel fueled transit buses using an on-road emissions measuring device known as the Portable Emissions Measurement System (PEMS). On-road study is important in terms of understanding the amount of emissions generated under the real traffic and environmental conditions. Emissions were measured on buses fueled with regular diesel (B0), B10 blend (10% biodiesel + 90% diesel) and B20 blend (20% biodiesel + 80% diesel). This paper demonstrates the use of hybrid soft-computing techniques such as the neuro-fuzzy technique for developing emissions prediction models from real-world data. Hybrid soft-computing techniques have been shown to work well in handling data prone to noise and uncertainty, which is characteristic of real-world scenario. Two neuro-fuzzy methodologies were considered in this study: the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS). A brief review of model development, recommended parametric settings, and statistical evaluation of prediction performance of both techniques are discussed. In general, the ANFIS showed better prediction accuracy for the individual emissions compared to DENFIS although the prediction accuracies are comparable.

scholarly journals Water temperature prediction in a subtropical subalpine lake using soft computing techniques

2016 ◽  
Vol 20 (2) ◽  
pp. 1 ◽  
Author(s):  
Saeed Samadianfard ◽  
Honeyeh Kazemi ◽  
Ozgur Kisi ◽  
Wen-Cheng Liu
Keyword(s):  
Water Temperature ◽  
Soft Computing ◽  
Water Surface ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

Lake water temperature is one of the key parameters in determining the ecological conditions within a lake, as it influences both chemical and biological processes. Therefore, accurate prediction of water temperature is crucially important for lake management. In this paper, the performance of soft computing techniques including gene expression programming (GEP), which is a variant of genetic programming (GP), adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) to predict hourly water temperature at a buoy station in the Yuan-Yang Lake (YYL) in north-central Taiwan at various measured depths was evaluated. To evaluate the performance of the soft computing techniques, three different statistical indicators were used, including the root mean squared error (RMSE), the mean absolute error (MAE), and the coefficient of correlation (R). Results showed that the GEP had the best performances among other studied methods in the prediction of hourly water temperature at 0, 2 and 3 meter depths below water surface, but there was a different trend in the 1 meter depth below water surface. In this depth, the ANN had better accuracy than the GEP and ANFIS. Despite the error (RMSE value) is smaller in ANN than GEP, there is an upper bound in scatter plot of ANN that imposes a constant value, which is not suitable for predictive purposes. As a conclusion, results from the current study demonstrated that GEP provided moderately reasonable trends for the prediction of hourly water temperature in different depths. ResumenLa temperatura del agua es uno de los parámetros básicos para determinar las condiciones ecológicas de un lago, ya que está influenciada por procesos químicos y biológicos. Además, la exactitud en la predicción de la temperatura del agua es esencial para el manejo del lago. En este artículo se evalúa el desempeño de técnicas de soft computing como la Programación de Expresiones de Genes (PEG), que es una variante de la Programación Genética (PG), el Sistema Neuro-fuzzy de Inferencia Adaptativa (Anfis, en inglés) y las Redes Neuronales Artificiales (RNA) para predecir la temperatura del agua en diferentes niveles de una estación flotante del lago Yuan-Yang (YYL), en el centro-norte de Taiwán. Se utilizaron tres indicadores estadísticos, el Error Cuadrático Medio (ECM), el Error Absoluto Medio (MAE, en inglés) y el Coeficiente de Correlación (R) para evaluar el desempeño de las técnicas de computación. Los resultados muestran que la PEG es más exacta en la predicción de la temperatura del agua entre 0,2 y 3 metros de profundidad. Sin embargo, se evidencia una tendencia diferente a partir del metro de profundidad. A esta distancia de la superficie, las RNA son más exactas que la PEG y el Anfis. Los resultados de este estudio probaron claramente la usabilidad del PEG y las RNA en la predicción de la temperatura del agua a diferentes profundidades.

Soft-Computing Control of Ball and Beam System

2018 ◽  
Vol 9 (4) ◽  
pp. 1-21 ◽  
Author(s):  
Ashwani Kharola ◽  
Pravin P. Patil
Keyword(s):  
Soft Computing ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Learning Ability ◽  
Gaussian Shape ◽  
Inference System ◽  
Neural Controllers ◽  
Beam System ◽  
Soft Computing Techniques ◽  
Ball And Beam System

This article derives a mathematical model and compares different soft-computing techniques for control of a highly dynamic ball and beam system. The techniques which were incorporated for control of proposed system were fuzzy logic, proportional-integral-derivative (PID), adaptive neuro fuzzy inference system (ANFIS) and neural networks. Initially, a fuzzy controller has been developed using seven gaussian shape membership functions. The article illustrates briefly both learning ability and parameter estimation properties of ANFIS and neural controllers. The results of PID controller were collected and used for training of ANFIS and Neural controllers. A Matlab simulink model of a ball and beam system has been derived for simulating and comparing different controllers. The performances of controllers were measured and compared in terms of settling time and steady state error. Simulation results proved the superiority of ANFIS over other control techniques.

scholarly journals DYNAMICAL CONTACT PARAMETER IDENTIFICATION OF SPINDLE-HOLDER-TOOL ASSEMBLIES USING SOFT COMPUTING TECHNIQUES

2020 ◽  
pp. 565
Author(s):  
Đorđe Čiča ◽  
Milan Zeljković ◽  
Saša Tešić
Keyword(s):  
Soft Computing ◽  
Fuzzy Inference ◽  
Contact Dynamics ◽  
Accurate Identification ◽  
Inference System ◽  
Contact Parameter ◽  
Soft Computing Techniques ◽  
Cutting Processes ◽  
Contact Parameters ◽  
Tool Assembly

In industry, the capability to predict the tool point frequency response function (FRF) is an essential matter in order to ensure the stability of cutting processes. Fast and accurate identification of contact parameters in spindle-holder-tool assemblies is very important issue in machining dynamics analysis. This work is an attempt to illustrate the utility of soft computing techniques in identification and prediction contact parameters of spindle-holder-tool assemblies. In this paper, three soft computing techniques, namely, genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) were used for identification of contact dynamics in spindle-holder-tool assemblies. In order to verify the proposed identification approaches, numerical and experimental analysis of the spindle-holder-tool assembly was carried out and the results are presented. Finally, a model based on the adaptive neural fuzzy inference system (ANFIS) was used to predict the dynamical contact parameters at the holder-tool interface of a spindle-holder-tool assembly. Accuracy and performance of the ANFIS model has been found to be satisfactory while validated with experimental results.

Cracked Rotor Diagnostics Using Soft Computing

2007 ◽  
Author(s):  
B. Samanta ◽  
C. Nataraj
Keyword(s):  
Soft Computing ◽  
Transient Response ◽  
Fuzzy Inference ◽  
Cracked Rotor ◽  
Inference System ◽  
Time Frequency ◽  
Wavelet Energy ◽  
Soft Computing Techniques ◽  
Detection And Diagnosis ◽  
Run Up

A study is presented for detection and diagnostics of cracked rotors using soft computing techniques like adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and genetic algorithms (GA). A simple model for a cracked rotor is used to simulate its transient response during startup for different levels of cracks. The transient response is processed through continuous wavelet transform (CWT) to extract time-frequency features for the normal and cracked conditions of the rotor. Several features including the wavelet energy distributions and the grey moment vectors (GMV) of the CWT scalograms are used as inputs for diagnosis of crack level. The parameters of the classifiers, ANFIS and ANN, along with the features from wavelet energy distribution and grey moment vectors are selected using GA maximizing the diagnostic success. The classifiers are trained with a subset of the data with known crack levels and tested using the other set of data (testing data), not used in training. The procedure is illustrated using the simulation data of a simple de Laval rotor with a ‘breathing’ crack for different crack levels during run-up through its critical speed. A comparison of diagnostic performance for the classifiers is presented. Results show the effectiveness of the proposed approach in detection and diagnosis of cracked rotors.

scholarly journals Detection of waterborne bacteria using Adaptive Neuro-Fuzzy Inference System

2020 ◽  
Vol 158 ◽  
pp. 05002
Author(s):  
Farhan Mohammad Khan ◽  
Smriti Sridhar ◽  
Rajiv Gupta
Keyword(s):  
Water Quality ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Analysis ◽  
Hybrid Technique ◽  
Membership Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

The detection of waterborne bacteria is crucial to prevent health risks. Current research uses soft computing techniques based on Artificial Neural Networks (ANN) for the detection of bacterial pollution in water. The limitation of only relying on sensor-based water quality analysis for detection can be prone to human errors. Hence, there is a need to automate the process of real-time bacterial monitoring for minimizing the error, as mentioned above. To address this issue, we implement an automated process of water-borne bacterial detection using a hybrid technique called Adaptive Neuro-fuzzy Inference System (ANFIS), that integrates the advantage of learning in an ANN and a set of fuzzy if-then rules with appropriate membership functions. The experimental data as the input to the ANFIS model is obtained from the open-sourced dataset of government of India data platform, having 1992 experimental laboratory results from the years 2003-2014. We have included the following water quality parameters: Temperature, Dissolved Oxygen (DO), pH, Electrical conductivity, Biochemical oxygen demand (BOD) as the significant factors in the detection and existence of bacteria. The membership function changes automatically with every iteration during training of the system. The goal of the study is to compare the results obtained from the three membership functions of ANFIS- Triangle, Trapezoidal, and Bell-shaped with 35 = 243 fuzzy set rules. The results show that ANFIS with generalized bell-shaped membership function is best with its average error 0.00619 at epoch 100.

scholarly journals Sistema de inferencia borroso basado en redes adaptativas para la evaluación de proyectos

2015 ◽  
Vol 19 (2) ◽  
pp. 53
Author(s):  
Anié Bermudez Peña ◽  
José Alejandro Lugo García ◽  
Pedro Yobanis Piñero Pérez
Keyword(s):  
Soft Computing ◽  
Key Management ◽  
Fuzzy Inference ◽  
Management Tool ◽  
Primary Data ◽  
Inference System ◽  
Computing Technique ◽  
Management Indicators ◽  
Soft Computing Techniques ◽  
Automated Tools

In this article, a set of key management indicators related to performance of execution, planning, costs, effectiveness, human resources, data quality, and logistics, are considered for the evaluation of a project. Several automated tools support project managers in this task. However, these tools are still insufficient to accurately assess projects in organizations with continuous improvement management styles and with presence of uncertainty in the primary data. An alternative solution is the introduction of soft computing techniques, allowing gains in robustness, efficiency, and adaptability in these tools. This paper presents an adaptivenetwork- based fuzzy inference system (ANFIS) to optimize projects evaluation made with the Xedro-GESPRO tool. The implementation of the system allowed the adjustment of fuzzy sets parameters in the inference rules for the assessment of projects, based on the automatic calculation of indicators. The contribution of this research lies in the application of ANFIS soft computing technique to optimize the evaluation of projects integrated with the management tool. The results contribute to the improvement of existing decision-making support tools into organizations towards project-oriented production. 

scholarly journals Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization

2021 ◽  
Vol 13 (8) ◽  
pp. 4576
Author(s):  
Muhammad Izhar Shah ◽  
Taher Abunama ◽  
Muhammad Faisal Javed ◽  
Faizal Bux ◽  
Ali Aldrees ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Predictive Models ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Surface Water Quality ◽  
Inference System ◽  
Input Optimization ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier removal and an exhaustive search method for selecting essential modeling inputs. Thereafter, the adaptive neuro-fuzzy inference system (ANFIS) was applied for modeling electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River. A larger dataset of a 30-year historical period, measured monthly, was utilized in the modeling process. The prediction capacity of the developed models was estimated by statistical assessment indicators. Moreover, the 10-fold cross-validation method was carried out to address the modeling overfitting issue. The results of the input optimization indicate that Ca2+, Na+, and Cl− are the most relevant inputs to be used for EC. Meanwhile, Mg2+, HCO3−, and SO42− were selected to model TDS levels. The optimum ANFIS models for the EC and TDS data showed R values of 0.91 and 0.92, and the root mean squared error (RMSE) results of 30.6 µS/cm and 16.7 ppm, respectively. The optimum ANFIS structure comprises a hybrid training algorithm with 27 fuzzy rules of triangular fuzzy membership functions for EC and a Gaussian curve for TDS modeling, respectively. Evidently, the outcome of the present study reveals that the ANFIS modeling, aided with data pre-processing and input optimization, is a suitable technique for simulating the quality of surface water. It could be an effective approach in minimizing modeling complexity and elaborating proper management and mitigation measures.

scholarly journals Penentuan Keputusan Pemberian Pinjaman Kredit Usaha Rakyat Menggunakan Metode Adaptive Neuro Fuzzy Inference System (Studi Kasus: BRI Unit A. Yani Bontang)

2020 ◽  
Vol 15 (2) ◽  
pp. 66
Author(s):  
Wahyu Dyan Permana ◽  
Indah Fitri Astuti ◽  
Heliza Rahmania Hatta
Keyword(s):  
Soft Computing ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Kredit Usaha Rakyat (KUR) merupakan program pemerintah yang termasuk dalam kelompok program penanggulangan kemiskinan berbasis pemberdayaan usaha ekonomi mikro dan kecil. Bank Rakyat Indonesia (BRI) unit A.Yani Bontang merupakan salah satu bank penyedia pemberian modal KUR yang pada 1 tahun terakhir kredit macet sebesar 1.2 % dari total pinjaman yang didistribusikan. Sistem Pendukung Keputusan (SPK) berbasis soft computing metode ANFIS dapat membantu masalah pemberian pinjaman dengan memberikan alternatif keputusan yang dapat membantu mengefesienkan waktu dalam pengambilan keputusan oleh bank. ANFIS merupakan sistem hybrid yang menggabungkan kelebihan antara sistem fuzzy dan jaringan syaraf tiruan. Variabel input yang digunakan adalah penghasilan, tempat tinggal, jumlah tanggungan, jaminan, serta lama usaha dan output adalah keputusan diterima atau ditolaknya pengajuan pinjaman oleh debitur. Hasil uji coba pelatihan mengunakan jenis membership function yang paling efektif adalah jenis Generalized Bell dengan hasil rata-rata error sebesar 8.3278 x10-7. Metode ANFIS dapat digunakan dalam memberikan keputusan pemberian KUR dengan baik sesuai dengan jenis membership function dan iterasi pada tahap pelatihan jaringan.

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