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Generating suitable game scenarios that can cater for individual players has become an emerging challenge in procedural content generation. In this paper, we propose a data-driven scenario generation framework for game-based training. An evolutionary scenario generation process is designed with a fitness evaluation methodology that integrates the processes of AI player modeling, simulation and model...
It is of great significance to carry out cities' air quality forecasting work for the prevention of the air pollution in urban areas and to the improvement of the living environment of urban residents. The air quality index (AQI) is a dimensionless index that quantitatively describes the state of air quality. In this paper, the data of air quality in Lanzhou released by china air quality online monitoring...
Obesity is an increasingly prevalent metabolic disorder, which results in increased risk of various diseases. One such disease is the coronary artery disease, which is the most common type of heart disease. Coronary artery disease (CAD) leads to the blockage of the arteries, that supply blood to the heart muscles, due to the accumulation of cholesterol and other material called plaque on the inner...
Energy crisis and environmental pollution stimulate the rapid development of new energy electric vehicles. The state of charge(SOC) is a key parameter of power battery in application, so the accurate estimation is extremely important. Factors affecting the battery SOC are many and complicated, scholars have proposed many methods to estimate SOC, but still does not solve the accuracy and practicability...
To determine investment and cost estimation scientifically and simplify the investment estimating preparation, an improved BP neural network estimation model with GA optimization is proposed, based on the learning process of standard BP neural network. Our scheme set initial weight and whitening positioning coefficient as genetic population. The coefficients are optimized according to the principle...
In this paper, we propose an improved Elman neural network model which contains a new feedback mechanism composed of a special external feedback we proposed and inherent internal feedback. In order to guarantee the generalization ability of the established model, we adopt Genetic Algorithm to optimize initial connection weights and number of hidden layer nodes at the same time. This kind of improved...
This work proposes a hybrid methodology for the detection and prediction of Extreme Significant Wave Height (ESWH) periods in oceans. In a first step, wave height time series is approximated by a labeled sequence of segments, which is obtained using a genetic algorithm in combination with a likelihood-based segmentation (GA+LS). Then, an artificial neural network classifier with hybrid basis functions...
Artificial Neural Networks often suffer from overfitting, both when trained through backpropagation or evolved through a Genetic Algorithm. An attempt at mitigating the overfitting of GA-evolved ANNs is made by using High-Probability Mutation (≈0.95) on binary-encoded ANN weights. The benchmark used is predicting the evolution of an Internet social network using real-world data. A lower bound is put...
HIV-1 integrase (IN) has become an important target for antiviral drug discovery. While AIDS drug treatment often fails due to the emergence of drug resistant species. Elvitegravir (EVG) is one of the FDA-approved drugs. We developed a neural network prediction model to make a qualitative EVG resistance phenotype prediction. First, we developed a genotype-phenotype database. Secondly, we classified...
The initial weights and thresholds of BP neural network are optimized by genetic algorithm in advance, and then the BP neural network is used in the prediction model of the postgraduate entrance examination results. Experimental results show that the optimized prediction model due to overcome the slow convergence speed and the defects of easy to produce local minimum has better accuracy than prediction...
In this study, we propose a new hybrid approach for time series prediction based on the efficient capabilities of fuzzy cognitive maps (FCMs) with structure optimization algorithms and artificial neural networks (ANNs). The proposed structure optimization genetic algorithm (SOGA) for automatic construction of FCM is used for modeling complexity based on historical time series, and artificial neural...
During the recent decade, the tourism industries have evolved evidently and quickly, pushing the bar higher for managers attempting to gain a competitive advantage in this industries. This study applies a combined model that combines the ACF, neural networks, and genetic algorithms for forecasting the tourism demand. Analytical results obtained using the ACF are entered as the input information of...
In this paper, a new model combining neural networks with genetic algorithm is proposed to solve the problem of waste water discharge optimization. Firstly we apply resilient backpropagation(RPROP) neural networks to water quality daily data prediction based on water quality and waste water discharge history data, then through genetic algorithm process concerning water quality influence and economic...
The financial market is complex, evolving and dynamic system, which has an extremely non-linear movement. Thus, investment return prediction represents a significant challenge, especially because of its great diversity, unsteadiness and unstructured data with a high degree of instability and pronounced hidden connections. It is known that accurate prediction of the stock market indexes is very important...
Analyzing inflation forecast problem, this paper proposes a SVM-based approach. Firstly, the paper reviews some former studies about inflation forecasting and predicting methodology, finding that SVM is a nonlinear adaptive data-driven model with strong approximation and generalization ability, which can be applied to complex forecasting tasks. Secondly, the paper establishes a SVM model and discusses...
High concentration photovoltaic is a new type of solar power generation mode, which has better photoelectric conversion rate but is more vulnerable to weather factors. Therefore, accurate and efficient forecasting methods have important significance of increasing the security and stability of the solar power station. This paper focuses on the short-term forecasting method which aims at forecasting...
A method to predict the displacement of landside, genetic algorithm based on wavelet neural network (GAWNN) model, is presented in this paper. The hybrid model improves the predicting precision, which is compared with genetic algorithm based on back-propagation neural network (GABPNN). Furthermore, the hybrid model is applied for predicting the displacement of Baishuihe landslides in the Three Gorges...
The development and application of Micro-Blog have made it simpler and easier for people to express their feelings. However, with the characteristics of openness and fast-spreading, Micro-Blog can quickly turn a point of view into public opinion. Therefore, Micro-blog opinion evolution and trends prediction have become the focus of people's attention. This paper is intended Micro-Blog public opinion...
Wavelet neural network (WNN) is widely used in wind power prediction because of its good self-learning capacity and excellent performance to approach any nonlinear function. However it also has limitation in precision and operating speed. This paper proposes a new genetic algorithm of wavelet neural network (GAWNN) for short-term wind power forecasting in electrical power systems. GAWNN makes a good...
Electric load forecasting plays a critical role for the reliable and efficient operation of power grids. In this paper we propose a load forecasting model using parallel radial basis function neural networks (RBFNN). The proposed implementation of RBFNN allows parallel computation therefore expedites the convergence of training process. The proposed model also employs a new hybrid chaotic genetic...
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