11,212 research outputs found
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
Journal 201
Penguasaan kemahiran generik di kalangan graduan hospitaliti di politeknik : satu kajian berkenaan keperluan industri perhotelan, persepsi pensyarah dan pelajar
Kajian yang dijalankan ini bertujuan untuk mengenal pasti kepentingan
kemahiran generik mengikut keperluan industri perhotelan di Malaysia dengan persepsi pensyarah dan persepsi pelajar Jabatan Hospitaliti. Oleh kerana matlamat kurikulum pendidikan kini adalah untuk melahirkan graduan yang dapat memenuhi keperluan pihak industri, maka kajian ini dijalankan untuk menilai hubungan di antara keperluan industri perhotelan di Malaysia dengan persepsi pensyarah dan pelajar Jabatan Hospitaliti di Politeknik. Terdapat 13 kemahiran generik yang diperolehi daripada Kementerian Pelajaran dan Latihan Ontario (1997) dijadikan
sebagai skop kepada instrumen kajian. Responden kajian terdiri daripada tiga pihak utama iaitu industri perhotelan di Malaysia yang melibatkan 40 buah hotel yang diwakili oleh MAH Chapter dan jawatankuasa dalam Malaysian Associated of Hotel (MAH), pensyarah Unit Hotel dan Katering dan pelajar semester akhir Diploma Hotel dan Katering di Politeknik Johor Bahru, Johor dan Politeknik Merlimau, Melaka. Kajian rintis yang dijalankan menunjukkan nilai Alpha Cronbach pada 0.8781. Data yang diperolehi dianalisis secara deskriptif dan inferensi dengan menggunakan perisian Statistical Package for Social Science (SPSS) versi 11.5. Melalui dapatan kajian, satu senarai berkenaan kemahiran generik yang diperlukan
oleh industri perhotelan telah dapat dihasilkan. Selain itu, senarai kemahiran generik menurut persepsi pensyarah dan juga persepsi pelajar turut dihasilkan. Hasil statistik dan graf garis yang diperolehi menunjukkan terdapat perbezaan di antara kemahiran generik yang diperlukan oleh industri perhotelan di Malaysia dengan kemahiran generik menurut persepsi pensyarah dan persepsi pelajar Politeknik. Dapatan analisis menggunakan korelasi Pearson mendapati bahawa tidak terdapat
perhubungan yang signifikan di antara kemahiran generik yang diperlukan oleh industri perhotelan dengan persepsi pensyarah dan persepsi pelajar. Namun begitu, terdapat hubungan yang signifikan di antara persepsi pensyarah dengan persepsi pelajar berkenaan dengan amalan kemahiran generik di Politeknik
Application of Computational Intelligence Techniques to Process Industry Problems
In the last two decades there has been a large progress in the computational
intelligence research field. The fruits of the effort spent on the research in the discussed
field are powerful techniques for pattern recognition, data mining, data modelling, etc.
These techniques achieve high performance on traditional data sets like the UCI
machine learning database. Unfortunately, this kind of data sources usually represent
clean data without any problems like data outliers, missing values, feature co-linearity,
etc. common to real-life industrial data. The presence of faulty data samples can have
very harmful effects on the models, for example if presented during the training of the
models, it can either cause sub-optimal performance of the trained model or in the worst
case destroy the so far learnt knowledge of the model. For these reasons the application
of present modelling techniques to industrial problems has developed into a research
field on its own. Based on the discussion of the properties and issues of the data and the
state-of-the-art modelling techniques in the process industry, in this paper a novel
unified approach to the development of predictive models in the process industry is
presented
Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay
U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models
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Novel fuzzy logic controllers with self-tuning capability
Two controllers which extend the PD+I fuzzy logic controller to deal with the plant having time varying nonlinear dynamics are proposed. The adaptation ability of the first self tuning PD+I fuzzy logic controller (STPD+I_31) is achieved by adjusting the output scaling factor automatically thereby contributing to significant improvement in performance. Second controller (STPD+I_9) is the simplified version of STPD+I_31 which is designed under the imposed constraint that allows only minimum number of rules in the rule bases. The proposed controllers are compared with two classical nonlinear controllers: the pole placement self tuning PID controller and sliding mode controller. All the controllers are applied to the two-links revolute robot for the tracking control. The tracking performance of STPD+I_31 and STPD+I_9 are much better than the pole placement self tuning PID controller during high speed motions while the performance are comparable at low and medium speed. In addition, STPD+I_31 and STPD+I_9 outperform sliding mode controller using same method of comparison study
An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants
We present a design method for iterative learning control system by using an output recurrent neural network (ORNN). Two ORNNs are employed to design the learning control structure. The first ORNN, which is called the output recurrent neural controller (ORNC), is used as an iterative learning controller to achieve the learning control objective. To guarantee the convergence of learning error, some information of plant sensitivity is required to design a suitable adaptive law for the ORNC. Hence, a second ORNN, which is called the output recurrent neural identifier (ORNI), is used as an identifier to provide the required information. All the
weights of ORNC and ORNI will be tuned during the control iteration and identification process,
respectively, in order to achieve a desired learning performance. The adaptive laws for the weights
of ORNC and ORNI and the analysis of learning performances are determined via a Lyapunov
like analysis. It is shown that the identification error will asymptotically converge to zero and
repetitive output tracking error will asymptotically converge to zero except the initial resetting
error
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