Neuro-Fuzzy-based Improved IMC for Speed Control of Nonlinear Heavy Duty Vehicles

A neuro-fuzzy based improved internal model control (I-IMC) is proposed for speed control of uncertain nonlinear heavy duty vehicle (HDV) as the standard IMC (S-IMC) can’t tackle the nonlinear systems effectively and degrades the performance of HDV system. Adaptive neuro-fuzzy inference system and artificial neural network with adaptive control are used for the design of I-IMC. The proposed control techniques are developed to achieve the better speed tracking performance and robustness of HDV system under the influence of road grade disturbance

Modeling of Optimized Neuro-Fuzzy Logic Based Active Vibration Control Method for Automotive Suspension

In this thesis, an active vibration control system was developed. The control system was developed and tested using a quarter car model of an adaptive suspension system. For active vibration control, an actuator was implemented in addition to the commonly used passive spring damper system. Due to nature of unpredictability of force required two different fuzzy inference system (FIS) were developed for the actuator. First a sequential fuzzy set was built, that resulted lower vertical displacement compared to basic damper spring model, but system had limited effect with disturbances of higher magnitude and continuous vibrations (rough road). To improve the performance of the sequential fuzzy set, the main fuzzy set was improved using an adaptive neuro fuzzy inference system (ANFIS). This model increased the performance substantially, especially for rough road and high magnitude disturbance scenarios. Finally, the suspension’s spring constant and damping co-efficient was optimized using a genetic algorithm to further improve the vibration control properties to achieve a balance of both ride stability and comfort. The final result is improved performance of the suspension system

Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength

Fuzzy Neural Networks (FNNs) with the integration of fuzzy logic, neural networks and optimization techniques have not only solved the issue of “black box” in Artificial Neural Networks (ANNs) but also have been effective in a wide variety of real-world applications. Despite of attracting researchers in recent years and outperforming other fuzzy inference systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rule-base optimization methods to perform efficiently when the number of inputs increase. Many researchers have trained ANFIS parameters using metaheuristic algorithms but very few have considered optimizing the ANFIS rule-base. Mine Blast Algorithm (MBA) which has been improved by Improved MBA (IMBA) can be further improved by modifying its exploitation phase. This research proposes Accelerated MBA (AMBA) to accelerate convergence of IMBA. The AMBA is then employed in proposed effective technique for optimizing ANFIS rule-base. The ANFIS optimized by AMBA is used employed to model classification of Malaysian small medium enterprises (SMEs) based on strength using non-financial factors. The performance of the proposed classification model is validated on SME dataset obtained from SME Corporation Malaysia, and also on real-world benchmark classification problems like Breast Cancer, Iris, and Glass. The performance of the ANFIS optimization by AMBA is compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), MBA and Improved MBA (IMBA), respectively. The results show that the proposed method achieved better accuracy with optimized rule-set in less number of iterations

River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques

In this research an attempt is made to develop highly accurate river flow forecasting models. Wavelet multi-resolution analysis is applied in conjunction with artificial neural networks and adaptive neuro-fuzzy inference system. Various types and structure of computational intelligence models are developed and applied on four different rivers in Australia. Research outcomes indicate that forecasting reliability is significantly improved by applying proposed hybrid models, especially for longer lead time and peak values

PREDIKSI CURAH HUJAN MENGGUNAKAN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)<br><br>Prediction of Rainfall Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

ABSTRAKSI: Curah hujan adalah endapan atau deposit air dalam bentuk cair maupun padat yang berasal atmosfer. Informasi tentang banyaknya curah hujan adalah salah satu unsur penting dan besar pengaruhnya terhadap segala macam aktifitas kehidupan seperti: keselamatan masyarakat, produksi pertanian, perkebunan, perikanan, penerbangan, public service, dan lain sebagainya.Pada urutan waktu tersebut, seberapa besar jumlah curah hujan yang turun. Dan besarnya curah hujan yang turun tersebut setiap waktu tertentu adalah berbeda (non-linear). Sehingga dengan pola data yang non-linear, akan di prediksi berapa besarnya curah hujan pada waktu yang akan datang atau disebut juga time-series prediction.Adaptive Neuro Fuzzy Inference System (ANFIS) merupakan kombinasi dari Sistem Inferensi Fuzzy dengan Jaringan Syaraf Tiruan dimana nilai keanggotaan dari Sistem Inferensi Fuzzy akan diperbaiki melalui pembelajaran dengan Jaringan Syaraf Tiruan sehingga dapat memberikan tingkat akurasi yang lebih baik untuk suatu sistem prediksi.Tugas akhir ini mengimplementasikan arsitektur ANFIS untuk prediksi curah hujan untuk wilayah depok dengan menggunakan data curah hujan dasarian. Terlebih dahulu data curah hujan dibagi menjadi data latih dan data uji. Kemudian dilakukan pelatihan untuk mencari parameter-parameter yang akan digunakan pada saat pengujian. Setelah itu dilakukan pengujian dengan menggunakan parameter yang didapat dari pelatihan.Kata Kunci : : Sistem Inferensi Fuzzy, Adaptive Neuro Fuzzy Inference System (ANFIS), Jaringan Syaraf Tiruan, time-series prediction, Curah hujan.ABSTRACT: Rainfall is a deposit of water in liquid or solid form that originated the atmosphere. Information about the amount of rainfall is one important element and the greatest effect on all sorts of life activities such as: public safety, agricultural production, plantations, fisheries, aviation, public service, and others.At the time the order is, how big the amount of rainfall that fell. And the amount of rainfall that fell was any particular time is different (non-linear). So, with the pattern of non-linear data, will predict how much rainfall in the future or also known as time-series prediction.Adaptive Neuro Fuzzy Inference System (ANFIS) is a combination of Fuzzy Inference System with Artificial Neural Networks in which the membership value of the Fuzzy Inference System will be improved through learning with neural networks that can provide better accuracy for a prediction system.This final project implements the architecture of ANFIS to predict rainfall for the region depok using rainfall data. First, rainfall data divided into training data and test data. Then do the training to find the parameters that will be used during testing. After it was examined by using the parameters obtained from training.Keyword: Fuzzy Inference System, Adaptive Neuro Fuzzy Inference System (ANFIS), neural networks, time-series prediction, rainfall

Design an intelligent controller for full vehicle nonlinear active suspension systems

The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function

DETEKSI KANKER PAYUDARA PADA CITRA MAMOGRAM MENGGUNAKAN GRAY LEVEL DIFFERENCE METHOD DAN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

ABSTRAKSI: Kanker payudara merupakan penyakit yang banyak di derita oleh wanita. Kanker tersebut mengalami pertumbuhan secara tidak terkontrol pada jaringan payudara. Mammografi merupakan salah satu cara pemeriksaan payudara dengan menggunakan sinar x-ray dosis rendah yang dapat mendeteksi gejala kanker payudara sedini mungkin yang menghasilkan citra berbentuk .jpg, disebut mammogram.Tugas akhir ini bertujuan menghasilkan suatu alat bantu berbasis software untuk para radiolog dalam mendiagnosa citra mammogram serta mempermudah dalam mengklasifikasikan tipe kelainan kanker payudara ke dalam tiga kelas berdasarkan BIRADS, yaitu normal, jinak, dan ganas. Secara umum, pendeteksian kanker payudara ini terdiri dari 3 bagian utama, yaitu: preprocessing, ekstraksi ciri, dan klasifikasi. Preprocessing citra yang dilakukan terdiri atas operasi morfologi, labeling dan tresholding, cropping dan normalisasi, serta segmentasi watershed. Proses selanjutnya dilakukan ekstraksi ciri dengan pendekatan statistik menggunakan teknik Gray Level Difference Method (GLDM). Pada proses ekstraksi ciri akan dihasilkan ciri atau fitur tertentu yang kemudian akan dikenali dengan metode klasifikasi Adaptive Neuro Fuzzy Inference System (ANFIS). Adaptive Neuro-Fuzzy Inference System (ANFIS) merupakan kombinasi dari Sistem Inferensi Fuzzy dengan Jaringan Syaraf Tiruan (JST) dimana nilai keanggotaan dari Sistem Inferensi Fuzzy akan diperbaiki melalui pembelajaran JST sehingga dapat memberikan tingkat akurasi yang lebih baik untuk suatu sistem klasifikasi. Hasil pengujian sistem menunjukkan bahwa pada tahap pengujian dengan data uji, ANFIS mampu melakukan klasifikasi data citra mammogram dengan tingkat akurasi 76,67%. Kata Kunci : mammogram, ekstraksi ciri, GLDM, ANFIS.ABSTRACT: Breast cancer is a disease suffered by many women. Those Cancer is the uncontrolled growth of breast tissue. Mammography is one way of breast screening using low-dose x-ray beam that can detect symptoms of breast cancer as early as possible which results in image form. Jpg, called mammogram.The final project is to produce a software-based tool for the radiologist in diagnosing mammogram image and make it easier to classify the types of breast cancer abnormalities into three classes based on BIRADS, namely normal, benign, and malignant. In general, the detection of breast cancer is made up of 3 main parts, namely: preprocessing, feature extraction, and classification. Image preprocessing is performed consisting of morphological operations, labeling and tresholding, cropping and normalization, then watershed segmentation. Feature extraction process is performed by using a statistical approach to Gray Level Difference Method (GLDM) technique. In the extraction process will produce characteristics traits or features which will be recognized by the method of classification Adaptive Neuro Fuzzy Inference System (ANFIS). Adaptive Neuro-Fuzzy Inference System (ANFIS) is a combination of Fuzzy Inference System with Artificial Neural Network (ANN) in which the membership value of a Fuzzy Inference System will be improved through learning neural network so that it can provide a better accuracy rate for a classification system. The test results show that the system is in the testing phase with test data, ANFIS is able to classify the mammogram image data with an accuracy rate of 76.67%. Keyword: mammograms, feature extraction, GLDM, ANFIS

A novel technique for load frequency control of multi-area power systems

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability