Evaluation-Function-based Model-free Adaptive Fuzzy Control

Designs of adaptive fuzzy controllers (AFC) are commonly based on the Lyapunov approach, which requires a known model of the controlled plant. They need to consider a Lyapunov function candidate as an evaluation function to be minimized. In this study these drawbacks were handled by designing a model-free adaptive fuzzy controller (MFAFC) using an approximate evaluation function defined in terms of the current state, the next state, and the control action. MFAFC considers the approximate evaluation function as an evaluative control performance measure similar to the state-action value function in reinforcement learning. The simulation results of applying MFAFC to the inverted pendulum benchmark veriï¬ed the proposed scheme's efficacy

Adaptive Control with Approximated Policy Search Approach

Most of existing adaptive control schemes are designed to minimize error between plant state and goal state despite the fact that executing actions that are predicted to result in smaller errors only can mislead to non-goal states. We develop an adaptive control scheme that involves manipulating a controller of a general type to improve its performance as measured by an evaluation function. The developed method is closely related to a theory of Reinforcement Learning (RL) but imposes a practical assumption made for faster learning. We assume that a value function of RL can be approximated by a function of Euclidean distance from a goal state and an action executed at the state. And, we propose to use it for the gradient search as an evaluation function. Simulation results provided through application of the proposed scheme to a pole -balancing problem using a linear state feedback controller and fuzzy controller verify the scheme's efficacy

Desain Sistem Ukur Simultan dan Portabel Berbasis Mikrokontroler AT89C51

Pada penelitian ini, telah dilakukan perancangan dan pembuatan sistem ukur portabel yang simultan berbasis mikrokontroler. Alat tersebut dirancang sedemikian rupa agar dapat dihubungkan ke berbagai jenis sensor yang mempunyai keluaran berupa tegangan analog. Alat yang dibuat ini diharapkan dapat melakukan pengukuran secara simultan untuk jenis-jenis sensor tertentu yang telah dihubungkannya. Alat tersebut juga dirancang agar dapat merekam hasil pengukurannya untuk tiap interval waktu tertentu. Selain itu, alat ini dilengkapi dengan port RS232 yang dapat dihubungkan ke port serial sebuah PC, yang memungkinkan pengguna untuk mentransfer data hasil pengukurannya ke PC. Dari hasil uji coba alat, dapat diketahui bahwa alat tersebut dapat melakukan pengukuran beberapa variabel secara simultan, dapat melakukan perekaman data secara periodik, dengan interval waktu antara 1 detik sampai 12 ja m , dengan jumlah rekaman per kanal maksimum sebanyak 500 rekaman serta dapat melakukan komunikasi dengan PC untuk mentransfer data hasil pengukuran maupun perekaman

Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting

The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other well-known method in the literature

Pemilihan Pemasok Bahan Mentah Pada Restoran Menggunakan Metode Fuzzy Analytical Hierarchy Process

Production in the restaurant is a continuous production that must be provided with uncertain request based on the consumer demand. Availability of raw materials is preferred to support the production process. In purchasing management it is often difficult to choose the right supplier of raw materials for each process order. The purpose of this research is to implement the method of Fuzzy Analytical Hierarchy Process (FAHP) in the Decision Support System (DSS) purchase of raw materials to suppliers. The addition of fuzzy logic in AHP is used to enhance the accuracy of subjectivity in the process of purchasing management assessment for each criteria and alternatives.The analysis includes a comparison with the results of manual calculations based SPK FAHP and accuracy of the final system with expert recommendations for the management of the purchase order process of DSS.The results showed an increase in profit of the accuracy of the selection of suppliers restaurant using FAHP method compared with the manual method. The analysis shows a significant difference when applied to the same material, fixed price of each supplier but different number of purchases, able to provide the difference in price which means lower costs and increase profits purchase.Index Terms — DSS, FAHP, Supplier Management, Order Management, Raw Material, FAHP

Analisis Jarak Microphone Array Dengan Teknik Pemrosesan Sinyal Fast Fourier Transform Beamforming

The main problem in the application of the sound source detection is to estimate the angle of the wave or called as Direction Of Arrival (DOA) of Planewave. The method commonly to overcome the problem to utilize the sensor array by the data processing technique such as a beamforming technique. In this research was done by DOA estimation that used technique Fast Fourier Trasnfrom (FFT) beamformer with sensor configuration by Uniform Linear Array (ULA). The analysis has been to determine the distance of the array microphone that has DOA estimation with high accuration to the real source posisition. Based on the variation of distance of array microphone tested, shown the 6 cm is the bes distance which has the most dominant DOA estimation results with high accurac

Peramalan Penjualan Mobil Menggunakan Jaringan Syaraf Tiruan Dan Certainty Factor

Prediksi penjualan adalah salah satu cara untuk meningkatkan laba Perusahaan, peramalan diperlukan untuk menyetarakan antara perbedaan waktu yang sekarang dan yang akan datang terhadap kebutuhan, Jaringan Syaraf Tiruan (JST) dapat mengaplikasikan dengan baik metode peramalan.Pendekatan peramalan kuantitatif dengan metode times series akan menentukan nilai data masukan dari sekumpulan data serial atau berkala dari transaksi pada suatu jangka waktu tertentu. Data dibagi menjadi data pelatihan, pengujian dan validasi. Proses peramalan menggunakan metode certainty factor (CFf) sebagai nilai pembanding pada bobot koreksi yang telah di latih dalam jaringan backpropagation untuk prediksi yang optimal. Simulasi program peramalan penjualan mobil honda tahun 2015 dengan variabel input data penjualan daerah 30,000 unit, penjualalan dealer 25.000, penjualan tunai 25.000, CF = 0.5 dan kredit 19.000 menghasilkan ramalan penjualan sebanyak 29579 unit dengan target error 4,205 %.Kata Kunci—Peramalan, Time series, Certainty Factor, JST, Backpropagation

Penerapan Metode Hybrid Fuzzy C-Means Dan Particle Swarm Optimization (FCM - PSO) E Untuk Segmentasi Citra Geografis

Beberapa lapisan dari Sistem Informasi Geografis (SIG) bisa dibedakan oleh mata telanjang dari sebuah citra satelit namun pasti akan melelahkan jika mengamati citra begitu banyak. Penelitian ini dilakukan untuk melakukan otomasi pengamatan dengan metode segmentasi. Metode segmentasi yang diusulkan adalah Hybrid Fuzzy C-Means – Particle Swarm Optimization (FCM-PSO). Hasil penelitian menunjukkan FCM-PSO lebih unggul dari FCM biasa sekalipun dengan kelemahan waktu eksekusi yang lebih panjang.Kata Kunci—FCM, PSO, Segmentasi, SI

Development of empirical mode decomposition based neural network for power quality disturbances classification

The complexity of the electric power network causes a lot of distortion, such as a decrease in power quality (PQ) in the form of voltage variations, harmonics, and frequency fluctuations. Monitoring the distortion source is important to ensure the availability of clean and quality electric power. Therefore, this study aims to classify power quality using a neural network with empirical mode decomposition-based feature extraction. The proposed method consists of 2 main steps, namely feature extraction, and classification. Empirical Mode Decomposition (EMD) was also applied to categorize the PQ disturbances into several intrinsic mode functions (IMF) components, which were extracted using statistical parameters and the Hilbert transformation. The statistical parameters consist of mean, root mean squared, range, standard deviation, kurtosis, crest factor, energy, and skewness, while the Hilbert transformation consists of instantaneous frequency and amplitude. The feature extraction results from both parameters were combined into a set of PQ disturbances and classified using Multi-Layer Feedforward Neural Networks (MLFNN). Training and testing were carried out on 3 feature datasets, namely statistical parameters, Hilbert transforms, and a combination of both as inputs from 3 different MLFNN architectures. The best results were obtained from the combined feature input on the network architecture with 2 layers of ten neurons, by 98.4 %, 97.75, and 97.4 % for precision, recall, and overall accuracy, respectively. The implemented method is used to classify PQ signals reliably for pure sinusoids, harmonics with sag and swell, as well as flicker with 100 % precisio