The synergy effect's benefit is widely accepted. The object of this paper is to investigate whether a hybrid approach combining different stock prediction approaches together can dramatically outperform the single approach and compare the performance of different hybrid approaches. The hybrid model includes three well-researched algorithms: back propagation neural network (BPNN), adaptive network-based fuzzy neural inference system (ANFIS) and support vector machine (SVM). First, we utilize them independently to single-step forecast the stock price, and then integrate the three forecasts into a final result by a combining strategy. Two different combining methods are investigated. The first method is a linear combination of the three forecasts. The second method combines them by a neural network. We have all of the algorithms experiment on the S&P500 Index. The experiment verifies that by combining the single algorithm appropriately, better performance can be achieved.
Effective dynamic scheduling is an essential element in the process of intelligent road construction. The primary goal of this paper is to outline a two stage framework of dynamic scheduling for construction using layered fuzzy inference and radial basis function (RBF) neural network. The layered fuzzy inference presents an initial model which embeds the experts' knowledge by Zadah fuzzy theory and decision fusion. The RBF neural network adaptively adjusts the parameters of the initial model during the operation process. The experiment of the actual engineering problem shows that the scheduling results accord with the human knowledge and the training of the model needs less time compared with BP neural network. The proposed hybrid framework has been integrated in the practical asphalt road construction scheduling system.
The paper presents a type of open industrial robot which is designed for processing armor plates of ship. It is the first robot based on SERCOS in China. With a so-called water-fire procedure, the robot can make an original plate the desirable shape. To deal with the diversity of the plates and the unexpected distortion during the processing, we promote a method called "measure instruct processing". The implementation of self-adaptive processing is discussed in the article.
A new feature selection method named ReliefF-GA-Wrapper is proposed to combine the advantages of filter and wrapper. In the ReliefF-GA-Wrapper method, the original features are evaluated by the ReliefF method, and the resulting estimation is embedded into the genetic algorithm applied to search optimal feature subset with the train accuracy of induction learning algorithm for the evaluation function. Experiments are carried on handwritten Chinese characters dataset, which is a large-scale dataset, and several other typical datasets with features more than 20. The results show ReliefF-GA-Wrapper has better performance then ReliefF and GA-Wrapper, indicating that the proposed ReliefF-GA-Wrapper algorithm is competitive and scales well to large datasets.
This paper presents an open controller which is designed for bending the hull steel plates. It is an industrial robot that is based on the SERCOS bus and carries out a so-called "bending by line heating" procedure in China. To treat with the diversity of the original steel plates' shapes, we designed an open controller for it. The controller is open because it provides a set of instructions with which the end user can compile different programs according to the different working procedures. A positioning method used in the saddle type plate forming is also introduced.
The identification of Chinese herbal powders is usually based on physical or chemical detection, but that is far from enough to identity dozens of herbal species. Microscopic images of these powders contain variety of information, and important evidence for identification. These images usually contain variety of substance, and most of them are noises, which makes the target segmentation become a difficult job. An effective automatic target segmentation algorithm based on texture is proposed in this paper. Our method consists of two steps: "Preliminary Segmentation" and "Further Segmentation". Firstly, feature vector of texture is extracted and clustered into two groups: background and foreground; secondly, taking the continuity of edge and the locality of target into consideration, energy equations are established, and Maximum flow-Minimum cut Algorithm is applied to solve them. Three groups of images are used to test our method: microscopic images of Chinese herbal powders, Brodaze Images, and natural texture images. And the experimental results show that our method achieves a better segmentation compared with Grab-Cut, and additionally user inter-action is not required in our method.
Recognition of handwritten Chinese characters is a large-scale pattern recognition task, which is difficult and time consuming to build the corresponding classifiers. In this paper, two feature selection methods are proposed to reduce the complexity and speed up the handwritten Chinese recognition: one is the ReliefF-Wrapper method which evaluates the original features with the ReliefF method, and then uses the wrapper method to decide the number of features to be selected; and the other is GA-Wrapper that uses genetic algorithm to search the optimal subset of features with high training accuracy. Experiments were performed on 800 most frequently used Chinese characters, with 80,000 handwritten samples. Results show that the ReliefF-Wrapper method has good interpretation and high speed and GA-Wrapper gains higher accuracy. Limitations of the both methods and future work are also discussed.
Based on expert systems on a steel plate, an intelligent robot controller for steel plate line heating and cooling on a large complex curved surface is researched and designed, and hardware and software systems of the intelligent robot controller are described in detail. As a result, the designed robot controller has a good function and improves efficiency in shipbuilding manufacture.
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