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As it is well-known, orange peel is used for making jam and oil. For this purpose, orange samples with high peel thickness are best. In order to predict peel thickness in orange fruit, we present a system based in image features, comprising: area, eccentricity, perimeter, length/area, blue value, green value, red value, wide, contrast, texture, wide/area, wide/length, roughness, and length. A novel...
In this paper, a new method based on Group Method of Data Handling (GMDH), Improved Particle Swarm Optimization (PSO), and Least Squares Support Vector Machine (LSSVM) is proposed to solve the problem in power load forecasting, which is difficult to determine the input node and model parameters of minimum support vector machine (LSSVM) modeling. The specific method is as follows: firstly, the authors...
This paper proposes a novel short-term load forecasting (STLF) method based on extreme learning machine (ELM) and improved gravitational search algorithm (IGSA). The IGSA is used to search the optimal set of input weights and hidden biases for the ELM, improving the basic gravitational search algorithm (GSA) by involving the ability of exploitation in particle swarm optimization (PSO). Based on the...
Trustworthiness is an important indicator for service selection and recommendation in the cloud environment. However, predicting the trust rate of a cloud service based on its multifaceted quality of services (QoSs) is not an easy task due to the complicated and non-linear relations between service’s QoS values and the final trust rate of the service. According to the existing studies, the adoption...
A temperature prediction model based on Self-adaption Particle Swarm Optimization (SAPSO) and Extreme Learning Machine (ELM) is proposed in this paper. The nano-iron powder decomposing furnace temperature prediction model is established based on ELM. ELM, a neural network, is developed rapidly in recent years, but it requires a lot of hidden layer neurons to achieve ideal prediction accuracy. In order...
In this paper, a novel random neural network (RNN) model based optimization process for radiator-based heating system is proposed to maintain a comfortable indoor environment in a living room of a single storey residential building. The predictive model of the living room is developed by training a feed forward RNN and then optimisation algorithms are used to calculate the optimal flowrate for the...
This paper focuses on the problem of company financial risk warning, which is of great importance in modern company management. As BP neural network is a powerful tool to make state forecasting in complex system, in this paper, we propose a new company financial risk warning approach based on BP neural network. After demonstrating the main characterics of BP neural network, the proposed algorithm...
Accurate and robust load forecasting models play an important role in power system planning. Due to smaller size and inherent property of good classification, Radial Basis Function Neural Network (RBFNN) is always preferred over other neural network structures. It is used by researchers as an effective tool for Short-Term Load Forecasting (STLF). The smaller size of this network may lead its output...
To resolve the problem of short-term power load forecasting, we propose a self-adapting particle swarm optimization (PSO) algorithm to optimize the error back propagation (BP) neural network model. The proposed model is called PSO-BP model which employs PSO to adjust control parameters of BP neural network. In order to verify the performance of PSO-BP, the practical datum of a city in China are selected...
Exchange rate forecasting involves many challenges in research. Due to the difficulty of selecting superior variables to design a good forecasting mode, few empirical studies have discussed the influence of explainable variables. In this paper, a new forecasting model is constructed; we adopt the particle swarm optimization (PSO) to select the optimal input layer neurons to predict NTD/USD exchange...
This paper presents a novel dynamic neural network (DNN) predictive control strategy based on modified particle swarm optimization (PSO) for long time delay nonlinear process. The proposed dynamic NN structure could approximate to the actual system model and obtain the pure delay time exactly. An improved version of the original PSO is put forward to train the parameters of NN to enhance the convergence...
Based on rough set theory, a multilayer back propagation neural network (BPNN) whose parameters will be trained and optimized by particle swarm optimization (PSO) is presented here. Making use of the intelligence of RS in knowledge acquisition aspect, this method carries out a pretreatment on the BPNN data, extracts the regulation from large amount of original data, predigests the nerve basics in...
This paper presents a comprehensive study of forecasting a day-ahead of load and locational marginal pricing (LMP) using artificial intelligent systems. An artificial neural network (ANN) is trained with a stochastic optimization technique called particle swarm optimization (PSO). This training algorithm works to adjust the network weights and biases as to minimize the error function. Wavelet transformed...
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