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Monotonic type-2 fuzzy neural network and its application to thermal comfort prediction

NEURAL COMPUTING & APPLICATIONS 2013年第7-8期23卷 1987-1998页

作者： Li, Chengdong Yi, Jianqiang Wang, Ming Zhang, Guiqing Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

This paper studies the monotonic type-2 fuzzy neural network (T2FNN), which can be adopted in many identification and prediction problems where the monotonicity property between the inputs and outputs is required. Sufficient conditions on the parameters of the T2FNN are first presented to ensure the monotonicity between the inputs and outputs. Then, data-driven design model for the monotonic T2FNN is built. Also, under the monotonicity constraints, a hybrid algorithm is provided to optimize the parameters of the monotonic T2FNN. This hybrid algorithm utilizes the constrained least squares method and the penalty function-based gradient descent algorithm to realize reasonable parameter initialization and optimization. At last, an application to the thermal comfort index prediction is given to verify the effectiveness of the monotonic T2FNN. Comparisons with other methods are also made.

关键词： Type-2 fuzzy Neural network Monotonicity Constrained least squares method gradient descent algorithm Thermal comfort

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Hierarchical wavelet packet fuzzy inference system for pattern classification and system identification

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2013年第1期44卷 109-126页

作者： Sharifi, A. Shoorehdeli, M. Aliyari Teshnehlab, M. Islamic Azad Univ Sci & Res Branch Dept Comp Tehran Iran KNT Univ Technol Mechatron Dept Tehran Iran KNT Univ Technol Dept Elect Engn Tehran Iran

This study presents a hierarchical Takagi-Sugeno-Kang type fuzzy system called hierarchical wavelet packet fuzzy inference system. In the proposed method, wavelet packet transform is applied on the input data to produce approximation and detail sub-bands of the input data and the output is used as the input vector of the proposed network. This network uses a hierarchical structure same as wavelet packet decomposition tree, in which adaptive network-based fuzzy inference system is used as sub-model. Also, gradient descent algorithm is chosen for training the parameters of antecedent and conclusion parts of the sub-models. In order to evaluate the capability of the proposed method, its applications in pattern classification, system identification and time-series prediction have been studied. The results show that the proposed method performs better than the other conventional models.

关键词： wavelet transform hierarchical fuzzy model gradient descent algorithm pattern classification system identification time-series prediction

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Kernel regression with sparse metric learning

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2013年第4期24卷 775-787页

作者： Huang, Rongqing Sun, Shiliang E China Normal Univ Dept Comp Sci & Technol Shanghai 200241 Peoples R China

Kernel regression is a popular non-parametric fitting technique. It aims at learning a function which estimates the targets for test inputs as precise as possible. Generally, the function value for a test input is estimated by a weighted average of the surrounding training examples. The weights are typically computed by a distance-based kernel function and they strongly depend on the distances between examples. In this paper, we first review the latest developments of sparse metric learning and kernel regression. Then a novel kernel regression method involving sparse metric learning, which is called kernel regression with sparse metric learning (KR_SML), is proposed. The sparse kernel regression model is established by enforcing a mixed (2, 1)-norm regularization over the metric matrix. It learns a Mahalanobis distance metric by a gradient descent procedure, which can simultaneously conduct dimensionality reduction and lead to good prediction results. Our work is the first to combine kernel regression with sparse metric learning. To verify the effectiveness of the proposed method, it is evaluated on 19 data sets for regression. Furthermore, the new method is also applied to solving practical problems of forecasting short-term traffic flows. In the end, we compare the proposed method with other three related kernel regression methods on all test data sets under two criterions. Experimental results show that the proposed method is much more competitive.

关键词： Kernel regression sparse metric learning mixed norm regularization gradient descent algorithm traffic flow forecasting

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IMPROVED ONLINE IDENTIFICATION OF ACOUSTIC MISO SYSTEMS BASED ON SEPARATED INPUT SIGNAL COMPONENTS

IMPROVED ONLINE IDENTIFICATION OF ACOUSTIC MISO SYSTEMS BASE...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Thuene, Philipp Enzner, Gerald Ruhr Univ Bochum Inst Commun Acoust IKA Bochum Germany

ISBN: (纸本)9781479903566

Creating an immersive listening experience and providing the audience with improved spatial realism is the goal of many adaptive audio reproduction techniques such as room equalization or crosstalk cancellation. The majority of these approaches currently relies on acoustic impulse responses (AIRs) that have been measured prior to the actual audio reproduction. In order to maintain a high degree of adaptivity, however, the AIRs need to be estimated online during the reproduction process, which turns out to be a severely ill-conditioned problem due to the high inter-channel correlation of the loudspeaker signals. In this paper, we present a novel approach to MISO system identification with realistically correlated excitation. Based on the idea of separate treatment of correlated and uncorrelated signal components, we propose two extended filter structures for gradient-descent-based adaptive system identification and provide theoretical analysis and experimental validation of their effectiveness.

关键词： room acoustics multichannel acoustic system identification nonuniqueness gradient descent algorithm

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ENERGY MINIMIZATION-BASED MIXTURE MODEL FOR IMAGE SEGMENTATION

ENERGY MINIMIZATION-BASED MIXTURE MODEL FOR IMAGE SEGMENTATI...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiao, Zhiyong Adel, Mouloud Bourennane, Salah Ecole Cent Marseille Inst Fresnel F-13013 Marseille France Univ Aix Marseille Inst Fresnel F-13013 Marseille France

ISBN: (纸本)9781479903566

A novel mixture model with spatial constraint is proposed for image segmentation. This model assumes that the pixel label prior probabilities are similar if the pixels are geometric close. An energy function is defined on the spatial space for measuring the spatial information. We also derive an energy function on the observed data space from the log-likelihood function of the standard mixture model. We estimate the model parameters and posterior probability by minimizing the combination of the two energy functions, using the gradient descent algorithm. Numerical experiments are presented where the proposed method is tested on synthetic and real world images. These experimental results demonstrate that the proposed method achieves competitive performance compared to spatially variant finite mixture model.

关键词： Energy minimization mixture model spatial information gradient descent algorithm image segmentation

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Performance Evaluation of Generalized ADALINE Neural Configuration with Variable Learning-rate Parameter

Performance Evaluation of Generalized ADALINE Neural Configu...

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作者： Savita Thapar University

学位级别：硕士

Artificial Neural Network (ANN) is a mathematical model that is inspired by the structured and/or functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. Recent advances in the software and hardware technologies of neural networks have motivated new studies in the architecture and applications of these networks. Neural networks have potentially powerful characteristics, which can be utilized in the development of our research goal, namely, a true autonomous machine. Machine learning is a major step in this development. The immense computational power of modern digital machines has increased the feasibility of implementing closed-loop control systems in many applications. The main focus of this thesis work is completed on the performance evaluation ADALINE neural configuration for system identification of dynamical systems, noise cancellation and adaptive prediction. It is well known that ADALINE is slow in the convergence which is not appropriate for the online applications and identification of the time varying systems. Two techniques are proposed to speed up the convergence of learning, thus increase the tracking capability of the time varying system parameters. One idea is to introduce a momentum term to the weight adjustment during convergence period. The other technique is to train the generalized ADALINE network multiple epochs with data from a sliding window of the system's input output data. We present an online identification method based on a generalized Adaptive Linear Element (ADALINE) neural network, called GADALINE, for linear time varying systems which can be described by a discrete time model. The fine tuned GADLINE is quite suitable for online system identification and real time adaptive control applications due to its low computational demand. GADALINE neural configuration with a variable learning-rate pa

关键词： ADALINE learning-rate parameter gradient descent algorithm MSE GADALINE

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IMPROVED ONLINE IDENTIFICATION OF ACOUSTIC MISO SYSTEMS BASED ON SEPARATED INPUT SIGNAL COMPONENTS

IMPROVED ONLINE IDENTIFICATION OF ACOUSTIC MISO SYSTEMS BASE...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Philipp Thune Gerald Enzner Institute of Communication Acoustics (IKA) Ruhr-Universit?t Bochum Germany

ISBN: (纸本)9781479903573

关键词： room acoustics multichannel acoustic system identification nonuniqueness gradient descent algorithm room acoustics multiple-input single-output signals ENTERING steepest descent algorithm System identification Audio

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Energy Aware Battery Powered Electric Vehicles:A Predictive Model Driven Approach

Energy Aware Battery Powered Electric Vehicles:A Predictive ...

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2013 2nd International Conference on Advances in Computer Science and Engineering(CSE 2013)

作者： Dennis Babu Anirudh Kumar Joydeb Roychowdhury Embedded Systems Laboratory CSIR- Central Mechanical Engineering Research Institute

Active energy management for power optimization is essential for successful commercialization of battery powered electric vehicles. In this paper we propose and implement an energy aware electric vehicle using an efficient battery management system, Eff BMS which estimates the remaining charge of the Li-ion battery online, learns the rate of change of SOC and accordingly modulates the velocity and acceleration of the vehicle for optimal energy usage. An Extended Kalman Filter(EKF) was used to estimate the remaining charge of the Li-ion battery pack. An online dual time frame gradient descent algorithm with higher time frame for updating dataset and lower time frame for parameter tuning learns the SOC curve and employs a set point controller for energy management in motion. The entire model was developed in a SPARTAN 6 LX-45 reconfigurable FPGA board and is tested in a laboratory model of differentially driven single geared electric car. Case study of an upward slope hill motion is performed and results shows 10-12% energy savings.

关键词： battery management system FPGA gradient descent algorithm Extended Kalman Filter EffBMS

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Efficient classification algorithm and a new training mode for the adaptive radial basis function neural network equaliser

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IET COMMUNICATIONS 2012年第2期6卷 125-137页

作者： Assaf, R. El Assad, S. Harkouss, Y. Zoaeter, M. Team Image Site Nantex HDR IETR Lab Intitut Elect & Telecommun Rennes IETR UMR CNRS 6164 Rennes France

The study presents a new classification algorithm and a new online training mode used for learning the parameters of a Bayesian RBFNN (radial basis function neural network) equaliser in a non-linear time-varying channel. The classification algorithm is used to determine the centres of the hidden layer neurons that are equal to the channel states. This proposed unsupervised classification algorithm is based on both the K-means and the rival penalised competitive algorithms. Its main advantage is neither an initialisation phase nor a knowledge of the channel states number is required. The connections of weights and the spread of the hidden neurons are learned by the gradient descent algorithm, which applies a new proposed training mode. This training mode combines the advantages of both the online and the offline training modes such as stability and good speed of convergence. The performances of the RBFNN equaliser trained by the proposed method are shown in comparison with the performances of the optimal Bayesian equaliser and those of the same equaliser trained by other known training modes. All these performances are studied by using different types of channels.

关键词： hidden layer neuron online training mode Communications computing Neural computing techniques offline training mode optimal Bayesian adaptive RBFNN equaliser gradient descent algorithm Knowledge engineering techniques adaptive equalisers belief networks time-varying channels parameter learning radial basis function networks gradient methods optimal adaptive Bayesian radial basis function neural network equaliser telecommunication computing unsupervised classification algorithm unsupervised learning K-means algorithm Interpolation and function approximation (numerical analysis) nonlinear time-varying channel Communication channel equalisation and identification rival penalised competitive algorithm

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Design of a Prediction Model for Cement Rotary Kiln Using Wavelet Projection Fuzzy Inference System

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CYBERNETICS AND SYSTEMS 2012年第5期43卷 369-397页

作者： Sharifi, A. Shoorehdeli, M. Aliyari Teshnehlab, M. Islamic Azad Univ Dept Comp Sci & Res Branch Tehran Iran KNT Univ Technol Mechatron Dept Tehran Iran KNT Univ Technol Dept Elect Engn Tehran Iran

In a cement factory, a rotary kiln is the most complex component and it plays a key role in the quality and quantity of the final product. This system involves complex nonlinear dynamic equations that have not been completely worked out yet. In conventional modeling procedures, a large number of the involved parameters are crossed out and an approximation model is presented instead. Therefore, the performance of the obtained model is very important and an inaccurate model may cause many problems in the design of a controller. This study presents a Takagi-Sugeno (TS)-type fuzzy system called a wavelet projection fuzzy inference system (WPFIS) in which a dimension reduction section is used at the input stage of the fuzzy system. In order to clarify the structure of the extracted features, structural learning with forgetting (SLF) based on Minkowski norms is proposed. In addition, gradient descent (GD) was used as a training algorithm. The results show that the proposed method has higher performance in comparison with conventional models. The data collected from Saveh White Cement Company were used in our simulations.

关键词： cement rotary kiln gradient descent algorithm Morlet wavelet pruning algorithm TS-type fuzzy system

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