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This paper proposes a novel method of Chinese word segmentation utilizing morphology information. The method introduces morphology into statistical model to capture structural relationship within word. It improves the conventional Conditional Random Fields (CRFs) models on the ability of representing the structure information. Firstly, a word-segmented Chinese corpus is annotated with morphology tags...
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...
In machine learning study, semi-supervised learning has received increasing interests in the last years. It is applied to classification problems where only a small portion of the data points is labeled. In these situations, the reliability of these labels is extremely important because it is common to have mislabeled samples in a data set and these may propagate their wrong labels to a large portion...
Semi-Supervised Learning (SSL) is a machine learning research area aiming the development of techniques which are able to take advantage from both labeled and unlabeled samples. Additionally, most of the times where SSL techniques can be deployed, only a small portion of samples in the data set is labeled. To deal with such situations in a straightforward fashion, in this paper we introduce a semi-supervised...
B-cell secreted antibodies play a critical role in fighting against the invaders and abnormal self tissues. Identifying the epitope on antigens recognized by the paratope on antibodies can enlighten the understanding of this important immune mechanism. Predicting B-cell epitope can also pave the way for vaccine design and disease therapy. However, due to the high complexity of this problem, previous...
Distribution Static Compensator (DSTATCOM) is a shunt compensation device which is generally used to solve power quality problems in distribution systems. In distribution power system, these power quality problems mainly arise due to the pulsed loads, which causes the degradation of the entire system performance. The control strategy of DSTATCOM plays an important role to meet the objectives. A novel...
Particle swarm optimization (PSO) is a recently developed simple and efficient optimization technique and has been applied widely to real life optimization problems. This paper presents an improved version of the original PSO called the cooperative random learning particle swarm optimization (CRPSO), which employs several sub-swarms to seek the space and uses a modified velocity updating equation...
In this paper, an self-organizing TSK-type fuzzy neural network is proposed for predicting the short-term traffic flow. The proposed fuzzy neural network is adaptively organized from the collected short-term traffic flow data. The whole process is divided into two stage, i.e., structure identification and parameter learning. In structure identification, the mean shift clustering algorithm performs...
A novel hybrid learning algorithm for designing a TSK-type recurrent fuzzy neural network (RFNN) is proposed in this paper. The whole designing process includes two stages, i.e., structure identification and parameter optimization. The structure identification includes mean shift clustering (MSC) and mean firing strength (MFS). The MSC is used to partition the input space and the mean firing strength...
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