Mean square error of the Empirical Transfer Function Estimator for stochastic input signals

Gempa bumi merupakan salah satu ancaman terbesar terhadap gedung, sehingga perlu untuk mendesain gedung dengan memperhitungkan pembebanan gempa bumi yang terjadi. Dengan bantuan software finite element dapat diperoleh respons struktur berupa displacement, velocity, dan acceleration yang terjadi akibat gempa bumi. Jaringan Saraf Tiruan (JST) merupakan salah satu metode yang dapat memprediksi kerusakan bangunan dengan memanfaatkan data respons struktur dengan waktu analisis yang relatif lebih singkat dibandingkan menganalisis struktur satu per satu. Penelitian ini bertujuan untuk menganalisis data gempa dengan magnitude intensitas tinggi yang berbeda-beda. Data input dan output diperoleh melalui software Finite Element untuk menghasilkan jumlah data yang diperlukan JST yaitu sebanyak 4489 data. Pada penelitian ini, komposisi yang digunakan untuk training, testing, dan validating adalah 60%, 25%, dan 15% masing – masingnya. Data input yang digunakan yaitu waktu, acceleration arah x dan y, velocity arah x dan y, serta displacement arah x dan y. Sedangkan untuk data target yang digunakan yaitu kinerja struktur yang ditentukan oleh FEMA 356 dan simpangan antar lantai arah x dan y. Hasil pengujian menunjukkan analisis oleh JST yang menggunakan transfer function Tan-Sigmoid menunjukkan nilai R2 sebesar 97,542% dan Mean Square Error (MSE) yang dihasilkan yaitu sebesar 1,2449.E-07. Hal ini menunjukkan analisis JST dengan transfer function Tan-Sigmoid dapat digunakan untuk memprediksi kinerja dari struktur dengan cepat dan akurat. Dengan demikian metode ini diharapkan dapat direkomendasikan untuk Structural Engineer dan perencana gedung dalam mendesain bangunan gedung bertingkat tahan gempa.

Download Full-text

Prediction of runoff using BPNN, FFBPNN, CFBPNN algorithm in arid watershed: A case study

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-200046 ◽

2020 ◽

Vol 24 (3) ◽

pp. 243-251

Author(s):

Sandeep Samantaray ◽

Abinash Sahoo

Keyword(s):

Neural Network ◽

Transfer Function ◽

Mean Square Error ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Coefficient Of Determination ◽

Hydraulic Structures ◽

Mean Square ◽

Feed Forward Back Propagation

Here, an endeavor has been made to predict the correspondence between rainfall and runoff and modeling are demonstrated using Feed Forward Back Propagation Neural Network (FFBPNN), Back Propagation Neural Network (BPNN), and Cascade Forward Back Propagation Neural Network (CFBPNN), for predicting runoff. Various indicators like mean square error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2) for training and testing phase are used to appraise performance of model. BPNN performs paramount among three networks having model architecture 4-5-1 utilizing Log-sig transfer function, having R2 for training and testing is correspondingly 96.43 and 95.98. Similarly for FFBPNN, with Tan-sig function preeminent model architecture is seen to be 4-5-1 which possess MSE training and testing value 0.000483, 0.001025, RMSE training and testing value 0.02316, 0.03085 and R2 for training and testing as 0.9925, 0.9611, respectively. But for FFBPNN the value of R2 in training and testing is 0.8765 0.8976. Outcomes on the whole recommend that assessment of runoff is suitable to BPNN as contrasted to CFBPNN and FFBPNN. This consequence helps to plan, arrange and manage hydraulic structures of watershed.

Download Full-text

The Bordeaux Automatic Meridian Circle

International Astronomical Union Colloquium ◽

10.1017/s0252921100074170 ◽

1978 ◽

Vol 48 ◽

pp. 227-228

Author(s):

Y. Requième

Keyword(s):

Mean Square Error ◽

Mean Square ◽

Meridian Circle ◽

The Mean ◽

Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

Download Full-text

MEAN SQUARE ERROR FOR UNIFORM–KERNEL ESTIMATE AND NEAREST NEIGHBOR ESTIMATE OF NONPARAMETRIC REGRESSION FUNCTIONS

Acta Mathematica Scientia ◽

10.1016/s0252-9602(18)30700-8 ◽

1985 ◽

Vol 5 (2) ◽

pp. 175-185

Author(s):

Dongchu Sun

Keyword(s):

Mean Square Error ◽

Nonparametric Regression ◽

Nearest Neighbor ◽

Mean Square ◽

Kernel Estimate ◽

Regression Functions

Download Full-text

Investigation of Accuracy of a Calibrated Estimator of a Ratio by Modelling

Nonlinear Analysis Modelling and Control ◽

10.15388/na.2005.10.4.15113 ◽

2005 ◽

Vol 10 (4) ◽

pp. 333-342

Author(s):

V. Chadyšas ◽

D. Krapavickaitė

Keyword(s):

Mean Square Error ◽

Taylor Series ◽

Random Sampling ◽

Finite Population ◽

Taylor Series Expansion ◽

Simple Random Sampling ◽

Population Parameter ◽

Mean Square ◽

Parameter Ratio ◽

Using Data

Estimator of finite population parameter – ratio of totals of two variables – is investigated by modelling in the case of simple random sampling. Traditional estimator of the ratio is compared with the calibrated estimator of the ratio introduced by Plikusas [1]. The Taylor series expansion of the estimators are used for the expressions of approximate biases and approximate variances [2]. Some estimator of bias is introduced in this paper. Using data of artificial population the accuracy of two estimators of the ratio is compared by modelling. Dependence of the estimates of mean square error of the estimators of the ratio on the correlation coefficient of variables which are used in the numerator and denominator, is also shown in the modelling.

Download Full-text

Performance analysis of global local mean square error criterion of stochastic linearization for nonlinear oscillator

Vietnam Journal of Mechanics ◽

10.15625/0866-7136/12015 ◽

2019 ◽

Author(s):

Nguyen Cao Thang ◽

Luu Xuan Hung

Keyword(s):

Performance Analysis ◽

Mean Square Error ◽

Nonlinear Oscillators ◽

Degree Of Freedom ◽

Equivalent Linearization ◽

Mean Square ◽

Error Criterion ◽

Stochastic Linearization ◽

Local Mean ◽

Mean Square Error Criterion

The paper presents a performance analysis of global-local mean square error criterion of stochastic linearization for some nonlinear oscillators. This criterion of stochastic linearization for nonlinear oscillators bases on dual conception to the local mean square error criterion (LOMSEC). The algorithm is generally built to multi degree of freedom (MDOF) nonlinear oscillators. Then, the performance analysis is carried out for two applications which comprise a rolling ship oscillation and two degree of freedom one. The improvement on accuracy of the proposed criterion has been shown in comparison with the conventional Gaussian equivalent linearization (GEL).

Download Full-text

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

Engineering and Technology Journal ◽

10.30684/etj.v38i2a.319 ◽

2020 ◽

Vol 38 (2A) ◽

pp. 255-264

Author(s):

Hanan A. R. Akkar ◽

Sameem A. Salman

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mean Square Error ◽

Average Rate ◽

Mean Square ◽

Performance Measurements ◽

Artificial Intelligent ◽

Average Mean Square Error ◽

Artificial Neural ◽

Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

Download Full-text

Performance enhancement of Echo Cancellation Using a Combination of Partial Update ( PU) Methods and New Variable Length LMS (NVLLMS) Algorithm

Journal of Engineering ◽

10.31026/j.eng.2018.05.05 ◽

2018 ◽

Vol 24 (5) ◽

pp. 66

Author(s):

Thamer M. Jamel ◽

Faez Fawzi Hammood

Keyword(s):

Mean Square Error ◽

Return Loss ◽

Performance Enhancement ◽

Variable Length ◽

Echo Cancellation ◽

Convergence Time ◽

Length Variation ◽

Mean Square ◽

Step Size ◽

Echo Return Loss Enhancement

In this paper, several combination algorithms between Partial Update LMS (PU LMS) methods and previously proposed algorithm (New Variable Length LMS (NVLLMS)) have been developed. Then, the new sets of proposed algorithms were applied to an Acoustic Echo Cancellation system (AEC) in order to decrease the filter coefficients, decrease the convergence time, and enhance its performance in terms of Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE). These proposed algorithms will use the Echo Return Loss Enhancement (ERLE) to control the operation of filter's coefficient length variation. In addition, the time-varying step size is used.The total number of coefficients required was reduced by about 18% , 10% , 6%, and 16% using Periodic, Sequential, Stochastic, and M-max PU NVLLMS algorithms respectively, compared to that used by a full update method which is very important, especially in the application of mobile communication since the power consumption must be considered. In addition, the average ERLE and average Mean Square Error (MSE) for M-max PU NVLLMS are better than other proposed algorithms.

Download Full-text

Prediksi Daya Panel Surya Kapasitas 50 Wp Menggunakan Model Regresi Linier Majemuk

Jurnal Teknologi Bahan dan Barang Teknik ◽

10.37209/jtbbt.v10i2.166 ◽

2020 ◽

Vol 10 (2) ◽

Author(s):

Radian Indra Mukromin ◽

Muhammad Khamim Asy'ari

Keyword(s):

Mean Square Error ◽

Mean Square

Sistem monitoring daya listrik pada panel surya penting dilakukan. Hal ini disebabkan daya listrik panel surya dapat mempengaruhi performansi pengisian baterai dan keandalan dari panel surya. Sifat stokastik dari temperatur panel surya dan iradiasi surya mengakibatkan fluktuasi daya listrik, sehingga diperlukan sistem prediksi daya panel surya. Sistem prediksi dapat dirancang untuk mendapatkan model prediksi daya panel surya secara matematik menggunakan model regresi linier majemuk. Model dibangun untuk sistem prediksi dengan menggunakan data latih dari keluaran panel surya. Variasi yang diberikan adalah jenis variabel masukan untuk membangun model. Variabel masukan model terdiri dari temperatur panel surya, iradiasi surya, dan kombinasi dari keduanya. Pengujian data dilakukan dengan menggunakan uji korelasi majemuk, uji signifikasi regresi linier majemuk, dan uji signifikasi koefisien regresi. Hasil perancangan sistem prediksi terbaik adalah kombinasi temperatur panel surya dan iradiasi surya sebagai variabel masukan. Nilai MSE(mean square error) terkecil sebesar 9,83 untuk data latih dan 22,73 untuk data uji.

Download Full-text