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Genetic Algorithms (GAs) and structured Genetic Algorithms (sGAs) are powerful tools for modelling some of the activities related to the conceptual stage of the design process. Artificial Neural Networks (ANNs) are Artificial Intelligence (AI) tools which can learn and generalise from examples and experience to produce meaningful solutions to problems even when input data is fuzzy, discontinuous or...
There has been an increase in the number of domains researching bio-inspired algorithms. The researches conclusions mostly suggest that by taking an example of the processes that work in nature for so long can have its benefits. They proved their performance is greater then the classic approach in most fields. Basically, they were applied to optimize other processes. In this paper we will study and...
This paper presents using bootstrap aggregated neural networks for the modelling and optimization control of reactive polymer composite moulding processes. Neural network models for the degree of cure are developed from process operational data. To improve model generalization capability, multiple neural networks are developed from bootstrap re-samples of the original data and are combined. Optimal...
The technique of ice storage is the uppermost technical measure for the future in China, which can realize the “peak load shifting” and the Demand-Side Management, meliorate the contradictions between providing and demanding. Based on the ASHRAE (American Society of Heating, Refrigeration, and Air Conditioning Engineers) coefficient method and ANN (Artificial Neural Networks) method, the hourly temperature...
The instability of the concentration of CO2 in the system of CO2 refining is controlled by the following means: recognizing the system by neural net work; building the prediction model and the technological parameters optimization model of the system; predicting the key producing parameters which affect the concentration of CO2 the most; optimizing and controlling the key producing parameters by the...
The underwater environment poses a difficult challenge for autonomous underwater navigation. A standard problem of underwater vehicles is to maintain its position at a certain depth in order for it to perform desired operations. An effective controller is required for this purpose and hence the design of a depth controller for an Unmanned Underwater Vehicle is described in this paper. The control...
After studying the disadvantage of BP neural network which has low convergent speed and trap into local minima easily, an idea of designing a new hybrid neural network model. By using Artificial Bee Colony Algorithm (ABC) to expand the updated space of weight and using the fitness functions to decide the better weight. On the basis, make the acquired better value as the weight of BP neural network...
Out of several antenna design techniques the Artificial Neural Network (ANN) based method is suitable for prediction of characteristic parameters of loop antenna by considering transmit - receive conditions of practical communication set-ups. The predicted set of parameters can be used to fix dimensions of a loop antenna which involves theoretical calculations. This work proposes an approach to determine...
Training of parameters in RBF neural network, this article proposes an optimization of RBF neural network parameters algorithm, which can overcome the disadvantages of select of data center and weights in RBF neural network. The algorithm process input data normalization and compute network output and hidden layer output angle cosine firstly, a set of data being established as the network center when...
This paper takes a kind of ceramic glaze as an example, and builds an improved BP neural network model for optimizing the formulation on ceramic glaze. The improved BP neural network adopts Levenberg-Marquardt algorithms. The paper reviews how to build the ceramic formulation optimization model based on BP artificial neural network, including the establishment of neural network, the training, and...
A GPU-accelerated OpenCL implementation of a back-propagation artificial neural network for the creation of QSAR models for drug discovery and virtual high-throughput screening is presented. A QSAR model for HSD achieved an enrichment of 5.9 and area under the curve of 0.83 on an independent data set which signifies sufficient predictive ability for virtual high-throughput screening efforts. The speed-up...
The present paper proposes a new intelligent strategy that is suitable for optimal design of large scale complex systems such as ships and aircrafts. The strategy relies on three key aspects. The first is choosing a suitable compact compressed invertible representation for the high dimensional design variables vector. The second is defining a goal-focused problem-specific objective function. The third...
We have previously documented the on-going work in the EUFORIA project to parallelise and optimise European fusion simulation codes, see. This involves working with a wide range of codes to try and address any performance and scaling issues that these codes have. However, as no two simulation codes are exactly the same, it is very hard to apply exactly the same approach to optimising a disparate range...
Based on improved Elman neural network, establish soft sensor model for the station boiler pollution monitoring, which provides a theoretical basis for boiler soot blowing optimization strategy. Intelligent soot-blowing programmable control system combines the traditional blowing control with the soft sensor model, which is constructed by a modular approach, set online fouling monitor, data analysis,...
Topology optimization of P2P overlays has become an increasingly important issue in recent years. P2P systems today have an increased number of legal applications, but they still fail to meet two important requirements crucial for many such applications and for Internet providers: making efficient use of Wide-Area network resources and providing a fast response to queries by reducing routing stretch...
In future B3G/4G communication systems, the special application of femtocell in the enterprise offices and public places has broad prospect. However, as the femtocell operation under such multi-femtocell environment is significantly different from that of usual residential femtocells, the femtocell configuration and optimization might be much more complex. One key issue is to find how will the femtocell...
In this paper, artificial neural networks (ANNs) for modelling reflectarray periodic element is evaluated. A reflectarray antenna based on a 3-layer stacked patch element is chosen. Every element in the reflectarray must shift the phase of the reflection coefficient a given amount to obtain the prescribed radiation diagram. Different shifts are obtained from different geometrical configuration of...
Each HIV-1 patient has a diverse population of virus strains in his/her body as the virus quickly replicates and mutates, requiring a combination drug therapy optimized to the patient's unique viral population. Towards this goal, prediction systems have been developed to deduce the susceptibility of a given HIV genotype to a single drug. Many are rule-based systems or rely on hand-crafted features...
This paper presents a computational study on the performance of reliability measures by using optimized ANN for computer networks with fixed and varying link reliabilities. This objective of this paper is to focus on the design of minimum cost reliable computer networks when a set of nodes, their topology, and links are given to connect them. A comparative study of various approaches for evaluating...
Electricity market demands to the power industry in de-regulated form in this paper. The proposed load forecasting using ANN shows the effective risk management plans. This power market is to maintain their effective cost in terms of energy generation, energy purchase and optimization of the switching losses. This creates the need of load forecasting. So in this paper the load forecasting using ANN...
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