[PDF] Top 20 Model for tomato photosynthetic rate based on neural network with genetic algorithm

... of photosynthetic rate models have been established, including a rectangular hyperbolic model, a non-orthogonal rectangular hyperbolic model and an exponential relation model [9,10] ... See full document

... injury model is a hybrid technique of Artificial Neural Network with Genetic Algorithm (ANN-GA) to optimize the weight of Artificial Neural ...This model is used the VeR ... See full document

... solution. Genetic algorithm combined with another local search algorithm shows more efficient result could be ...of genetic algorithm with a local search algorithm called ... See full document

... intelligence based approach for text ...features. Genetic Algorithm is applied to extract the most relevant features for ...Artificial Neural Networks, which employs a different architecture ... See full document

... There are wide varieties of manufacturing and production ranges in which data mining is used. Data mining can be used a a supporting tool for determining causal relationships between input factors related to the ... See full document

... The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological ...The genetic ... See full document

... evolutionary algorithm (MOEA) coupled with a response surface approximation (RSA) surrogate ...of Genetic Algorithm (NSGA-II) coupled with the response surface ap- proximation (RSA) surrogate ...(EI) ... See full document

... artificial neural networks (ANN), which is a supervised, self-learning technique [8], [9], that can find the smooth approximation between input and output ...propagation neural network (BPN), which ... See full document

... BP neural network model based on principal component analysis (PCA) and genetic algorithm (GA) was proposed for the short-term prediction of gold ...casting model. The ... See full document

... use genetic algorithm optimization, consider the nonlinear neural network capacity, combining these and forming the method of evolutionary neural ...analysis model for ... See full document

... Support Vector Machine (SVM) considered as a classification technique that doesn’t require a statistical data model to reduce the classification errors immediately. This method will be popular when developed to ... See full document

... security model for multimedia cloud computing based on DES, 2-Fish, Genetic Algorithm and neural network based ...proposed algorithm is better ...of neural ... See full document

... mammograms based on thermal imaging technology by using an artificial neural network non-linear classification model, which was shown to have an accu- racy rate for breast cancer ... See full document

... communication network, this paper constructs the fault time-long forecasting index system from the two layers of transmission layer and network element layer, and quantifies each ...Then, based on ... See full document

... The model research based on the mechanism of temperature prediction has gradually ...Control based on intelligent algorithm is developing ...Bayesian network model[3], chaotic ... See full document

... Evolutionary Neural Network (NN) model to predict software ...proposed model is based on a hybrid intelligent technique wherein a neural network is trained for prediction ... See full document

... and genetic variation, codes a random solution of the problem as a “chromosome” to calculate its fitness, transfer the parameters by the selection, crossover and mutation of operators, and find the optimal ... See full document

... algorithms based on the evolution of a population toward a solution of a certain ...Artificial Neural Network (ANN) ...learning rate and momentum rate and also for weight ...learning ... See full document

... anomaly based IDS by considering the parameters of false positive rate and detection ...anomaly based intrusion detection system are genetic algorithm, apache storm, neural ... See full document

... to neural network function and biases are added to ...new network is analyzed for test data, if error is less than the previous one then values of weights and biases at that instant is taken as new ... See full document