18 Result(s)
within
Conference Paper
Wen-Yu **
Algorithm Analysis and Problem Complexity
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Chapter and Conference Paper
Fast Training of Deep LSTM Networks
Deep recurrent neural networks (RNN), such as LSTM, have many advantages over forward networks. However, the LSTM training method, such as backward propagation through time (BPTT), is really slow.
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Chapter and Conference Paper
Fuzzy Modeling from Black-Box Data with Deep Learning Techniques
Deep learning techniques have been successfully used for pattern classification. These advantage methods are still not applied in fuzzy modeling. In this paper, a novel data-driven fuzzy modeling approach is p...
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Chapter and Conference Paper
A New Approach to Detect Splice-Sites Based on Support Vector Machines and a Genetic Algorithm
This paper presents a method for classification of imbalanced splice-site classification problems, the proposed method consists of the generation of artificial instances that are incorporated to the dataset. A...
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Chapter and Conference Paper
Liver Cell Nucleuses and Vacuoles Segmentation by Using Genetic Algorithms for the Tissue Images
This paper proposes image segmentation methods for cell nucleuses and vacuoles in the liver fibrosis tissue images. The novel idea is to segment the objects by extracting the image features to determine the re...
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Chapter and Conference Paper
Selective Ensemble Modeling Parameters of Mill Load Based on Shell Vibration Signal
Load parameters inside the ball mill have direct relationships with the optimal operation of grinding process. This paper aims to develop a selective ensemble modeling approach to estimate these parameters. At...
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Chapter and Conference Paper
Neural Networks Training with Optimal Bounded Ellipsoid Algorithm
Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence, since it has a similar structure as Ka...
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Chapter and Conference Paper
Recurrent Fuzzy CMAC for Nonlinear System Modeling
Normal fuzzy CMAC neural network performs well because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension i...
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Chapter and Conference Paper
Integrated Analytic Framework for Neural Network Construction
This paper investigates the construction of a wide class of singlehidden layer neural networks (SLNNs) with or without tunable parameters in the hidden nodes. It is a challenging problem if both the parameter ...
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Chapter and Conference Paper
PD Control of Overhead Crane Systems with Neural Compensation
This paper considers the problem of PD control of overhead crane in the presence of uncertainty associated with crane dynamics. By using radial basis function neural networks, these uncertainties can be compen...
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Chapter and Conference Paper
Passivity Analysis for Neuro Identifier with Different Time-Scales
Many physical systems contains fast and slow phenomenons. In this paper we propose a dynamic neural networks with different time-scales to model the nonlinear system. Passivity-based approach is used to derive...
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Chapter and Conference Paper
Training Cellular Neural Networks with Stable Learning Algorithm
In this paper we propose a new stable learning algorithm for Cellular Neural Networks. Our approach is based on the input-to-state stability theory, so to obtain learning laws that do not need robust modificat...
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Chapter and Conference Paper
Passivity Analysis of Dynamic Neural Networks with Different Time-Scales
Dynamic neural networks with different time-scales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of the designed network be stable. In this paper, the ...
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Chapter and Conference Paper
Skeleton Pruning by Contour Partitioning
In this paper, we establish a unique correspondence between skeleton branches and subarcs of object contours. Based on this correspondence, a skeleton is pruned by removing skeleton branches whose generating p...
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Chapter and Conference Paper
Discrete-Time Sliding-Mode Control Based on Neural Networks
In this paper, we present a new sliding mode controller for a class of unknown nonlinear discrete-time systems. We make the following two modifications: 1) The neural identifier which is used to estimate the u...
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Chapter and Conference Paper
A Real Time MPEG-4 Parallel Encoder on Software Distributed Shared Memory Systems
This paper is dedicated to develo** real-time MEPG-4 parallel encoder on software distributed shared memory systems. Basically, the performance of a MPEG-4 parallel encoder implemented on distributed systems...
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Chapter and Conference Paper
System Identification Using Adjustable RBF Neural Network with Stable Learning Algorithms
In general, RBF neural network cannot match nonlinear systems exactly. Unmodeled dynamic leads parameters drift and even instability problem. According to system identification theory, robust modification term...
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Chapter and Conference Paper
Robust Adaptive Control Using Neural Networks and Projection
By using differential neural networks, we present a novel robust adaptive controller for a class of unknown nonlinear systems. First, dead-zone and projection techniques are applied to neural model, such that ...
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Chapter and Conference Paper
An Effective Molecular Algorithm for Solving the Satisfiability Problem
A well-defined satisfiability problem (SAT) is mapped into a unique expression of logical array by introducing a transformation. Such an expression forms a unique molecular algorithm for solving SAT, which is ...
