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Microbially assisted recovery of copper from low-grade chalcopyrite has been reported to be a very difficult process, conventional hydrometallurgical methods were limited by many parameters. This study focus on the design and the training of a Multi-Layer Perceptron classifier for the optimized preparation conditions for bioleaching of chalcopyrite. The proposed approach uses the heuristic Backpropagation...
Learning object categories from small samples is a challenging problem, where machine learning tools can in general provide very few guarantees. Exploiting prior knowledge may be useful to reproduce the human capability of recognizing objects even from only one single view. This paper presents an SVM-based model adaptation algorithm able to select and weight appropriately prior knowledge coming from...
An efficient differential recurrent neural network is developed in this paper, and the trained network can be used in the nonlinear model predictive control, and also predict the future dynamic behavior of the nonlinear process in real time. In the new training network, use Taylor series expansion and automatic differentiation techniques. The effectiveness of the differential recurrent neural network...
We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a multiple atlas method. For intensity information, nonparametric probability density function is used which considers intensity relation of different structures. To find a local minimum of...
Particle swarm optimization (PSO) is an algorithm modelled on swarm intelligence that finds a solution to an optimization problem in a search space. In this paper, a PSO-based artificial neural network algorithm is proposed to automatically grading the learning results. Basically, the PSO algorithm is utilized to adjust the connection weights of the selected ANN topology. Taken mandarin learning as...
The ability of Gaussian Process to flexibly and accurately fit arbitrary, even highly nonlinear data sets has lead to considerable interest in their application to many areas. Firstly, the usefulness of Gaussian Process models for application to complex systems metamodeling is proposed. Secondly, several approaches for training Gaussian Process models are examined, which include local optimization...
The concepts of symbolic dynamics and partitioning of time series data have been used for feature extraction and anomaly detection. Although much attention has been paid to modeling of finite state machines from symbol sequences, similar efforts have not been expended for partitioning of time series data to optimally generate symbol sequences. This paper addresses this issue and proposes a partitioning...
It was hard to set up an accurate mathematics model in cold rolling process and the structure of neural network was hard to confirm, so a CMAC neural network controller based on rough sets theory was proposed for flatness control. The original model of CMAC neural network flatness control was set up. Simultaneity dynamic learning rate was improved in the error correction algorithm of this neural network...
Soft capsule dropping pills product quality control system is a multi-input and multi-output complex system. First of all, the process parameters and a two-level hierarchy index system of soft capsule pills product quality were proposed based on the analysis to the production process. Then the model was established based on least squares support vector machine (LSSVM), whose inputs are the process...
In order to coordinate with and promote the scientific process of the national fencing team, we developed the decision support system for training. In fencing training, we established a two-way reasoning model based on Bayesian network and found the relationship between training process and physiological indicators. Combined with experienced knowledge and sample data, we did research on knowledge...
The dynamic performance of GAS OUTBURST is characterized by huge inertia, long time lag, nonlinear etc, adopting the support vector machines which is based on statistical theory and principle of minimizing structural risk, giving full play to its high-performance modeling ability and good fitting ability could effectively identify the gas outburst model so as to form the predictive model used for...
The paper introduces the random factor in Particle Swarm Optimization. Comparing with inertia weight, the particle's velocity is determined by previous velocity, own experience, public knowledge and random behavior. The random operator is similar with the mutation operator in the Genetic Algorithms. Simulation results show that the method introducing the random factor is better than inertia weight...
In this article is presented a method to design neural-genetic optimal controllers that are based on the fusion of a Recurrent Neural Network (RNN) and Genetic Algorithm (GA), these Computational Intelligence (CI) paradigms support the Linear Quadratic (LQR) design. The GA and RNN adaptation proprieties are the great advantage of the proposed approach, because all design is oriented to tune the optimal...
Because of the complexity of coal mine safety assessment and BP neural network's ability to handle complex problem and auto-adaptive ability, the application of BP neural network in the coal mine safety assessment is necessary and feasible. This paper proposes the principle of safety assessment index system establishment and establishs coal mine safety assessment index system, introduces neural network...
In metal cutting process, in order to improve the quality of product and raise the efficiency of equipment, new optimizations of cutting parameters method were put forward. Using three targets control parameters, machining time, machining accuracy and machining cost, multiple targets nonlinear programming model was established. The multiple targets nonlinear constraint program problem was transformed...
For the problem of local minimum for gradient descent method used to train an RBF (radial basis function) neural network, EP (evolutionary programming) is introduced to the training of RBF neural network in this paper. The combination method of EP and gradient descent method can effectively avoid local minimum, and provides a more reasonable network design. The effectiveness of the proposed scheme...
In Chinese township enterprises, multi-dimensional piece-rate wages calculation are very common. Because of the diverse types of products and the multi-dimensional floating price, the automatic wages-calculation system is needed. Considering that the rapid development of markets, thus, it is very important that the wages settlement systems to be carried out the adjustment and optimization of dynamic...
In recent years there is a growing interest in the study of sparse representation for signals. This article extends this research into a novel model for object classification tasks. In this model, we first apply the non-negative K-SVD algorithm to learning the discriminative dictionaries using very few training samples and then represent a test image as a linear combination of atoms from these learned...
A new fuzzy optimization neural network model is proposed based on the Levenberg-Marquardt (LM) algorithm on account of the disadvantages of slow convergence of traditional fuzzy optimization neural network model. In this new model, the gradient descent algorithm is replaced by the LM algorithm to obtain the minimum of output errors during network training, which changes the weights adjusting equations...
A Bayesian-Gaussian neural network (BGNN) method for nonlinear time variation system identification is proposed in this article. In the redefined BGNN training algorithms, the threshold matrix parameters are optimized by the swarm intelligence optimization algorithm(s) off-line and the sliding window data method are adopted for the BGNN on-line prediction. Some typical time-variation nonlinear systems...
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