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Dynamic Difficulty Adjustment (DDA) can adjust game difficulty level dynamically; so it generates a tailor-made experience for each gamer. If a game is too easy, the gamer will feel bored; if it is too hard, the gamer will become frustrated. DDA is a mechanism to overcome this dilemma and augment the entertainment of a game by dynamically adjusting the parameters, scenarios and behaviors in the game...
A novel image fusion algorithm using adaptive Unit-Linking Pulse Coupled Neural Networks (ULPCNN) is put forward. Firstly, ULPCNN threshold function is improved, and then the null interconnection and the adaptive interconnection ULPCNN are formed. Secondly, the nonlinear mapped ULPCNN time matrix can be obtained, which can represent the characteristic of the single pixel, but can reflect the pixel...
The ensemble method called Mixture of Experts, based on the Divide-and-Conquer principle was proposed to model a new Strabotomy Surgical Decision system, in which an extra gating component is used to compute weights dynamically according to the inputs. The aim is to develop a new operation quantity planning decision model (OQPDM) to predict the corrective quantity of lateral and medial rectus in strabotomy...
A novel image feature extraction and recognition algorithm, using adaptive Unit-Linking Pulse Coupled Neural Networks (AULPCNN), is put forward. Firstly, ULPCNN linking strength and threshold are improved based on take into account image local information, and then AULPCNN is formed. Secondly, the time matrix is come into being, which is a mapping from the spatial image information to time information...
We propose a novel texture feature extraction technique based on coefficients' co-occurrence histogram of discrete wavelet frame transformed image, which capture the information about relationship between each high frequency subband and the low frequency subband of the decomposed image at the corresponding level. It is not independently utilizing the information of each subband coefficient. The classification...
A novel method to detect small target embedded in sea clutter is presented for high frequency (HF) radar. The method is rooted in different characters between instantaneous radial velocity of sea current and moving target, and relies on the neural network for its implementation. By estimating the instantaneous velocity of sea current and target, we find that a spatial nonlinear model rather than deterministic...
A designing method for counter-propagation neural networks based on rough set theory is presented in this paper. Counter-propagation networks has been applied to various fields because of its topological construction closed to the mankindpsilas brain, while rough set theory has a powerful capability for qualitative analysis. By combining those advantages of the two theories, we can construct a kind...
The problem of image Gaussian noise filtering in the framework of Pulse Coupled Neural Network (PCNN) time matrix is addressed. The time matrix, generated by PCNN, contains useful information related to spatial structure of the image under processing. It is a mapping from image spatial information to time sequence. Through time matrix, Gaussian noisy pixels can be detected and then processed by using...
Based on the rough set theory, a counter propagation neural network algorithm for edge detection is presented in this paper. Firstly, a definition of rough membership function, which is used to modify the weigh values in the nomal counter propagation neural network, is proposed after introducing the rough set. Experiments show that the approach has achieved good results in improving the accuracy of...
BP algorithm has been widely used in calibrating measurement results detected by microwave resonator for improvement of accuracy. Conventional BP algorithm tends to get into infinitesimal locally, which worsens the stability of the measurement accuracy. An evolutionary neural network model based on IA-BP optimal algorithm is proposed in this paper. In the model, IA algorithm is first used for global...
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