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12th International Symposium on Quality Electronic Design (ISQED)

作者： Banerjee, Shayak Agarwal, Kanak B. Nassif, Sani R. Culp, James A. Liebmann, Lars W. Orshansky, Michael Univ Texas Austin Austin TX 78712 USA IBM Austin Res Lab Austin TX 78758 USA IBM Microelect East Fishkill NY 12533 USA

ISBN: (纸本)9781612849140

Design-manufacturing co-optimization has been earmarked as a key enabler of future technology scaling. Current manufacturing methods treat all transistors equally irrespective of their criticality in the design flow. In the presence of variations in the lithographic process, this leads to timing violations which reduces chip yield. In this paper, we develop a timing-driven process window optical proximity correction (TD-PWOPC) algorithm that tunes the mask generation for each transistor based on its electrical criticality in the design. Our method utilizes knowledge of timing information to generate delay bounds on each cell. It then develops a process variation aware OPC cost function for each cell to ensure that post-lithography delay lies within these bounds. This method uses a single image simulation coupled with simplified models for regular polysilicon layouts to predict though-process performance. We finally use a gradient-descent algorithm to minimize this cost function. Results show that the use of TD-PWOPC can reduce delay errors significantly compared to regular OPC at small runtime overheads of 4.5%.

关键词： Cost function Delay Lithography Logic gates Sensitivity TD-PWOPC Transistors chip yield cost function coupling timing objectives delay bounds design flow design for manufacture design-manufacturing co-optimization electrical criticality gradient descent algorithm gradient methods integrated circuit yield lithographic process mask generation post-lithography delay proximity effect (lithography) runtime overhead timing yield timing-driven process window optical proximity correction

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Sparse Kernel Regression for Traffic Flow Forecasting

Sparse Kernel Regression for Traffic Flow Forecasting

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8th International Symposium on Neural Networks

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

ISBN: (纸本)9783642210891

In this paper, a new kernel regression algorithm with sparse distance metric is proposed and applied to the traffic flow forecasting. The sparse kernel regression model is established by enforcing a mixed (2,1)-norm regularization over the metric matrix. It learns a mahalanobis metric by a gradient descent procedure, which can simultaneously remove noise in data and lead to a low-rank metric matrix. The new model is applied to forecast short-term traffic flows to verify its effectiveness. Experiments on real data of urban vehicular traffic flows are performed. Comparisons with two related kernel regression algorithms under three criterions show that the proposed algorithm is more effective for short-term traffic flow forecasting.

关键词： Traffic flow forecasting Kernel regression Sparse distance metric learning Mixed norm regularization gradient descent algorithm

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gradient descent Bit Flipping algorithms for Decoding LDPC Codes

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IEEE TRANSACTIONS ON COMMUNICATIONS 2010年第6期58卷 1610-1614页

作者： Wadayama, Tadashi Nakamura, Keisuke Yagita, Masayuki Funahashi, Yuuki Usami, Shogo Takumi, Ichi Nagoya Inst Technol Nagoya Aichi 4668555 Japan Meijo Univ Nagoya Aichi Japan

A novel class of bit-flipping (BF) algorithm for decoding low-density parity-check (LDPC) codes is presented. The proposed algorithms, which are referred to as gradient descent bit flipping (GDBF) algorithms, can be regarded as simplified gradient descent algorithms. The proposed algorithms exhibit better decoding performance than known BF algorithms, such as the weighted BF algorithm or the modified weighted BF algorithm for several LDPC codes.

关键词： LDPC code bit-flipping algorithm gradient descent algorithm

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Standalone CMAC Control System With Online Learning Ability

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2010年第1期40卷 43-53页

作者： Yeh, Ming-Feng Tsai, Cheng-Hung Lunghwa Univ Sci & Technol Dept Elect Engn Tao Yuan 33327 Taiwan Univ Sci & Technol China Dept Elect Engn Taipei 11581 Taiwan

A cerebellar model articulation controller (CMAC) control system, which contains only one single-input controller implemented by a differentiable CMAC, is proposed in this paper. In the proposed scheme, the CMAC controller is solely used to control the plant, and no conventional controller is needed. Without a preliminary offline learning, the single-input CMAC controller can provide the control effort to the plant at each online learning step. To train the differentiable CMAC online, the gradient descent algorithm is employed to derive the learning rules. The sensitivity of the plant, with respect to the input, is approximated by a simple formula so that the learning rules can be applied to unknown plants. Moreover, based on a discrete-type Lyapunov function, conditions on the learning rates guaranteeing the convergence of the output error are derived in this paper. Finally, simulations on controlling three different plants are given to demonstrate the effectiveness of the proposed controller.

关键词： Cerebellar model articulation controller (CMAC) dead-zone function gradient descent algorithm online learning ability signed distance

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SHA-Less Pipelined ADC Converting 10th Nyquist Band with In-Situ Clock-Skew Calibration

SHA-Less Pipelined ADC Converting 10th Nyquist Band with In-...

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32nd Annual Custom Integrated Circuits Conference CICC

作者： Huang, Pingli Hsien, Szukang Lu, Victor Wan, Peiyuan Lee, Seung-Chul Liu, Wenbo Chen, Bo-Wei Lee, Yung-Pin Chen, Wen-Tsao Yang, Tzu-Yi Ma, Gin-Kou Chiu, Yun Univ Illinois Urbana IL 61801 USA Texas Instruments Inc Sunnyvale CA USA Beijing Univ Technol Beijing Peoples R China Ind Technol Res Inst Hsinchu Taiwan

ISBN: (纸本)9781424457601

Conversion from dc to the 10th Nyquist band is enabled in a SHA-less, 10-b, 100-MS/s pipelined ADC by digitally calibrating the clock skew in the 3.5-b front-end stage. Architectural redundancy of pipelined ADC is exploited to extract skew information from the first-stage residue output with two out-of-range comparators and some simple digital logic;a gradient-descent algorithm is used to adaptively adjust the timing of the front-end sub-ADC to synchronize with that of the S/H. The 90-nm prototype consumes 12.2 mW and digitizes inputs up to 480 MHz (limited by testing equipment) without skew errors in experiments, whereas the same ADC fails at 130 MHz when the calibration is disabled. The measured SFDR is 71 dB at 20 MHz and 55 dB at 480 MHz.

关键词： Nyquist band SHA-less pipelined ADC adaptive timing adjustment analogue-digital conversion calibration clock skew clocks digital logic gradient descent algorithm gradient methods in-situ clock-skew calibration out-of-range comparator pipeline processing power 12.2 mW size 90 nm timing

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Model Predictive Assisting Control of Vehicle Following Task Based on Driver Model

Model Predictive Assisting Control of Vehicle Following Task...

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IEEE International Conference on Control Applications Part of 2010 IEEE Multi-Conference on Systems and Control

作者： Mikami, Koji Okuda, Hiroyuki Taguchi, Shun Tazaki, Yuichi Suzuki, Tatsuya Nagoya Univ Mech Sci & Engn Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan Toyota Cent Res & Dev Labs Inc Nagakute Aichi Japan JST CREST Program Tokyo Japan

ISBN: (纸本)9781424453634

A personalized driver assisting system that makes use of the driver's behavior model is developed. As a model of driving behavior, the Probability-weighted ARX (PrARX) model, a type of hybrid dynamical system models, is introduced. A PrARX model that describes the driver's vehicle-following skill on expressways is identified using a simple gradient descent algorithm from actual driving data collected on a driving simulator. The obtained PrARX model describes the driver's logical decision making as well as continuous maneuver in a uniform manner. Finally, the optimization of the braking assist is formulated as a mixed-integer linear programming (MILP) problem using the identified driver model, and computed online in the model predictive control framework.

关键词： MILP problem PrARX model driver behavior model driver information systems driver model driving simulator expressways gradient descent algorithm gradient methods logical decision making mixed-integer linear programming model predictive assisting control personalized driver assisting system predictive control probability-weighted ARX road vehicles vehicle following task vehicle-following skill

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TSK Interval Type-2 Fuzzy Neural Networks for Chaotic Time Series Prediction

TSK Interval Type-2 Fuzzy Neural Networks for Chaotic Time S...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Zhao, Liang Henan Univ Technol Inst Elect Engn Zhengzhou 450000 Peoples R China

ISBN: (纸本)9781424467129

This paper presents TSK interval type-2 fuzzy neural network (TSK IT2FNN) and its learning algorithm for chaotic time series prediction. First, The structure of TSK IT2FNN is decided using the hierarchical fuzzy clustering algorithm. Then its parameters of the precondition membership function and consequence weight are optimized using the gradient descent algorithm. Finally the effectiveness of IT2FNN and its learning algorithm are evaluated by using the Mackey-Glass chaotic time series. The simulation result shows that this proposed method is effective in the paper.

关键词： chaotic time series TSK interval type-2 fuzzy neural network hierarchical fuzzy clustering algorithm gradient descent algorithm

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Adaptive spherical Gaussian kernel in sparse Bayesian learning framework for nonlinear regression

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第2期36卷 3982-3989页

作者： Yuan, Jin Bo, Liefeng Wang, Kesheng Yu, Tao Norwegian Univ Sci & Technol Dept Prod & Qual Engn N-7491 Trondheim Norway Shanghai Univ CIMS Shanghai 200072 Peoples R China Shanghai Univ Robot Ctr Shanghai 200072 Peoples R China Xidian Univ Inst Intelligent Informat Proc Xian 710071 Peoples R China Shandong Agr Univ Sch Mech & Elect Engn Tai An 271018 Shandong Peoples R China

Kernel based machine learning techniques have been widely used to tackle problems of function approximation and regression estimation. Relevance vector machine (RVM) has state of the art performance in sparse regression. As a popular and competent kernel function in machine learning. conventional Gaussian kernel has unified kernel width with each of basis functions, which make impliedly a basic assumption: the response is represented below certain frequency and the noise is represented above such certain frequency. However, in many case, this assumption does not hold. To overcome this limitation, a novel adaptive spherical Gaussian kernel is utilized for nonlinear regression, and the stagewise optimization algorithm for maximizing Bayesian evidence in sparse Bayesian learning framework is proposed for model selection. Extensive empirical study, on two artificial datasets and two real-world benchmark datasets. shows its effectiveness and flexibility of model on representing regression problem with higher levels of sparsity and better performance than classical RVM. The attractive ability of this approach is to automatically choose the right kernel widths locally fitting RVs from the training dataset, which could keep right level smoothing at each scale of signal. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Relevance vector machine (RVM) Gaussian kernel function Regression gradient descent algorithm Bayesian evidence

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A fuzzy neighborhood-based training algorithm for feedforward neural networks

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NEURAL COMPUTING & APPLICATIONS 2009年第2期18卷 127-133页

作者： Ben Nasr, Mounir Chtourou, Mohamed ENIS Res Unit Intelligent Control Design & Optimizat C Dept Elect Engn Sfax 3038 Tunisia

In this work we present a new hybrid algorithm for feedforward neural networks, which combines unsupervised and supervised learning. In this approach, we use a Kohonen algorithm with a fuzzy neighborhood for training the weights of the hidden layers and gradient descent method for training the weights of the output layer. The goal of this method is to assist the existing variable learning rate algorithms. Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.

关键词： Feedforward neural network gradient descent algorithm Supervised and unsupervised learning Fuzzy self-organizing feature map Hybrid training

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A PSO-GD-BASED HYBRID algorithm FOR GENERAL FUZZY MEASURE DETERMINATION

A PSO-GD-BASED HYBRID ALGORITHM FOR GENERAL FUZZY MEASURE DE...

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International Conference on Machine Learning and Cybernetics

作者： Zhao, Huan-Yu Wang, Xi-Zhao Hebei Univ Coll Math & Comp Sci Key Lab Machine Learning & Computat Intelligence Baoding 071002 Hebei Peoples R China

ISBN: (纸本)9781424447053

Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identify fuzzy measure. However, there exist some limitations. In this paper, we design a hybrid algorithm called GDPSO, through introducing GD to PSO for the first time. This algorithm has the advantages of GD and PSO, and avoids the disadvantages of them. Theoretical analysis and experimental results verify this, and show that GDPSO is effective and efficient.

关键词： Fuzzy measure Fuzzy integral Particle swarm optimization gradient descent algorithm

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