Neural networks for cost estimation of shell and tube heat exchangers

The objective of this paper is to develop and test a model of cost estimating for the shell and tube heat exchangers in the early design phase via the application of artificial neural networks (ANN). An ANN model can help the designers to make decisions at the early phases of the design process. With an ANN model, it is possible to obtain a fairly accurate prediction, even when enough and adequate information is not available in the early stages of the design process. This model proved that neural networks are capable of reducing uncertainties related to the cost estimation of a shell and tube heat exchangers.     

An integrated approach for optimum design of bridge decks using genetic algorithms and artificial neural networks

The objective of this paper is to develop an integrated approach using artificial neural networks (ANN) and genetic algorithms (GA) for cost optimization of bridge deck configurations. In the present work, ANN is used to predict the structural design responses which are used further in evaluation of fitness and constraint violation in GA process. A multilayer back-propagation neural network is trained with the results obtained using grillage analysis program for different bridge deck configurations and the correlation between sectional parameters and design responses has been established. Subsequently, GA is employed for arriving at optimum configuration of the bridge deck system by minimizing the total cost. By integrating ANN with GA, the computational time required for obtaining optimal solution could be reduced substantially. The efficacy of this approach is demonstrated by carrying out studies on cost optimization of T-girder bridge deck system for different spans. The method presented in this paper, would greatly reduce the computational effort required to find the optimum solution and guarantees bridge engineers to arrive at the near-optimal solution that could not be easily obtained using general modeling programs or by trial-and-error.     

Design of ANN Based Non-Linear Network Using Interconnection of Parallel Processor

Suspicious mass traffic constantly evolves, making network behaviour tracing and structure more complex. Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them. They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results. Artificial neural network (ANN) offers optimal solutions in classifying and clustering the various reels of data, and the results obtained purely depend on identifying a problem. In this research work, the design of optimized applications is presented in an organized manner. In addition, this research work examines theoretical approaches to achieving optimized results using ANN. It mainly focuses on designing rules. The optimizing design approach of neural networks analyzes the internal process of the neural networks. Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters. The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues. The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors. The proposed ANN offered optimal results in real-world application problems, and the results were obtained using MATLAB.     

Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network

The ability of artificial neural networks (ANN) to model the unsteady aerodynamic force coefficients of flapping motion kinematics has been studied. A neural networks model was developed based on multi-layer perception (MLP) networks and the Levenberg–Marquardt optimization algorithm. The flapping kinematics data were divided into two groups for the training and the prediction test of the ANN model. The training phase led to a very satisfactory calibration of the ANN model. The attempt to predict aerodynamic forces both the lift coefficient and drag coefficient showed that the ANN model is able to simulate the unsteady flapping motion kinematics and its corresponding aerodynamic forces. The shape of the simulated force coefficients was found to be similar to that of the numerical results. These encouraging results make it possible to consider interesting and new prospects for the modelling of flapping motion systems, which are highly non-linear systems.     

Estimation of all-terminal network reliability using an artificial neural network

The exact calculation of all-terminal network reliability is an NP-hard problem, with computational effort growing exponentially with the number of nodes and links in the network. During optimal network design, a huge number of candidate topologies are typically examined with each requiring a network reliability calculation. Because of the impracticality of calculating all-terminal network reliability for networks of moderate to large size, Monte Carlo simulation methods to estimate network reliability and upper and lower bounds to bound reliability have been used as alternatives. This paper puts forth another alternative to the estimation of all-terminal network reliability — that of artificial neural network (ANN) predictive models. Neural networks are constructed, trained and validated using the network topologies, the link reliabilities, and a network reliability upperbound as inputs and the exact network reliability as the target. A hierarchical approach is used: a general neural network screens all network topologies for reliability followed by a specialized neural network for highly reliable network designs. Both networks with identical link reliability and networks with varying link reliability are studied. Results, using a grouped cross-validation approach, show that the ANN approach yields more precise estimates than the upperbound, especially in the worst cases. Using the reliability estimation methods of the ANN, the upperbound and backtracking, optimal network design by simulated annealing is considered. Results show that the ANN regularly produces superior network designs at a reasonable computational cost.Scope and purposeAn important application area of operations research is the design of structures, products or systems where both technical and business aspects must be considered. One expanding design domain is the design of computer or communications networks. While cost is a prime consideration, reliability is equally important. A common reliability measure is all-terminal reliability, the probability that all nodes (computers or terminals) on the network can communicate with all others. Exact calculation of all-terminal reliability is an NP-hard problem, precluding its use during optimal network topology design, where this calculation must be made thousands or millions of times. This paper presents a novel computationally practical method for estimating all-terminal network reliability. Is shown how a neural network can be used to estimate all-terminal network reliability by using the network topology, the link reliabilities and an upperbound on all-terminal network reliability as inputs. The neural network is trained and validated on a very minute fraction of possible network topologies, and once trained, it can be used without restriction during network design for a topology of a fixed number of nodes. The trained neural network is extremely fast computationally and can accommodate a variety of network design problems. The neural network approach, an upper bound approach and an exact backtracking calculation are compared for network design using simulated annealing for optimization and show that the neural network approach yields superior designs at manageable computational cost.     

Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

This paper presents a novel multi-objective genetic algorithm (MOGA) based on the NSGA-II algorithm, which uses metamodels to determine optimal sampling locations for installing pressure loggers in a water distribution system (WDS) when parameter uncertainty is considered. The new algorithm combines the multi-objective genetic algorithm with adaptive neural networks (MOGA–ANN) to locate pressure loggers. The purpose of pressure logger installation is to collect data for hydraulic model calibration. Sampling design is formulated as a two-objective optimization problem in this study. The objectives are to maximize the calibrated model accuracy and to minimize the number of sampling devices as a surrogate of sampling design cost. Calibrated model accuracy is defined as the average of normalized traces of model prediction covariance matrices, each of which is constructed from a randomly generated sampling set of calibration parameter values. This method of calculating model accuracy is called the ‘full’ fitness model. Within the genetic algorithm search process, the full fitness model is progressively replaced with the periodically (re)trained adaptive neural network metamodel where (re)training is done using the data collected by calling the full model. The methodology was first tested on a hypothetical (benchmark) problem to configure the setting requirement. Then the model was applied to a real case study. The results show that significant computational savings can be achieved by using the MOGA–ANN when compared to the approach where MOGA is linked to the full fitness model. When applied to the real case study, optimal solutions identified by MOGA–ANN are obtained 25 times faster than those identified by the full model without significant decrease in the accuracy of the final solution.     

Prediction of wind-induced pressures on a large gymnasium roof using artificial neural networks

The application of artificial neural networks (ANNs) to solve wind engineering problems has received increasing interests in recent years. This paper is concerned with developing two ANN approaches (a backpropagation neural network [BPNN] and a fuzzy neural network [FNN]) for the prediction of mean, root-mean-square (rms) pressure coefficients and time series of wind-induced pressures on a large gymnasium roof. In this study, simultaneous pressure measurements are made on a large gymnasium roof model in a boundary layer wind tunnel and parts of the model test data are used as the training sets for developing two ANN models to recognize the input–output patterns. Comparisons of the prediction results by the two ANN approaches and those from the wind tunnel test are made to examine the performance of the two ANN models, which demonstrates that the two ANN approaches can successfully predict the pressures on the entire surfaces of the large roof on the basis of wind tunnel pressure measurements from a certain number of pressure taps. Moreover, the FNN approach is found to be superior to the BPNN approach. It is shown through this study that the developed ANN approaches can be served as an effective tool for the design and analysis of wind effects on large roof structures.     

The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach

Decision-making in the early stages of a construction project will have a significant impact on the project. Limited and uncertain information, however, makes it difficult to accurately predict constriction costs. To solve this problem, this study developed the advanced case-based reasoning (CBR) model with 101 cases of multi-family housing projects.The advanced CBR model was developed to integrate the advantages of prediction methodologies such as CBR, multiple regression analysis (MRA), and artificial neural networks (ANN), and the optimization process using a genetic algorithm. This study defined four optimization parameters, as follows: (i) the minimum criterion for scoring the attribute similarity, (ii) the range of attribute weight, (iii) the range of case selection and (iv) the tolerance range of cross range between MRA and ANN. Since the system was developed using the Microsoft-Excel-based Visual Basic Application (VBA) for ease of use, it is expected that the model supports the stakeholders in charge of predicting and managing a construction cost in the early stages of a construction project to get more accurate result from historical cases as a reference.     

Artificial neural network modelling of the bioethanol-to-olefins process on a HZSM-5 catalyst treated with alkali

In this work the kinetic modelling of the transformation of bioethanol-to-olefins (BTO) process over a HZSM-5 catalyst treated with alkali using artificial neural networks (ANN) is presented. The main goal has been to obtain a BTO process neuronal model with the desired accuracy that allows the simplification and reduction of the computational cost with respect to a mechanistic knowledge model. To check the goodness of ANN base model structures, during the study a comparison with other alternative modelling techniques such as support vector machines was performed. Following a parameters optimization procedure and testing different training methods, the optimal ANN structure results to be a feed-forward 3–5–1 network with the Bayesian regularization training method. Using a set of experimental data obtained in a laboratory scale fixed bed reactor, we have obtained a similar fit to the knowledge model but with the advantage of being up to 43 times faster. These results are important for moving forward real time automatic control strategies in the biorefinery context.     

Control rules of aeration in a submerged biofilm wastewater treatment process using fuzzy neural networks

The present work is part of a global development of reliable real-time control and supervision tools applied to wastewater pollution removal processes. In these processes, oxygen is a key substrate in animal cell metabolism and its consumption is thus a parameter of great interest for the monitoring. In this paper, an integrated neural-fuzzy process controller was developed to control aeration in an Aerated Submerged Biofilm Wastewater Treatment Process (ASBWTP). In order to improve the fuzzy neural network performance, the self-learning ability embedded in the fuzzy neural network model was emphasized for improving the rule extraction performance. The fuzzy neural network proves to be very effective in modeling the aeration performs better than artificial neural networks (ANN).For comparing between operation with and without the fuzzy neural controller, an aeration unit in an Aerated Submerged Biofilm Wastewater Treatment Process (ASBWTP) was picked up to support the derivation of a solid fuzzy control rule base. It is shown that, using the fuzzy neural controller, in terms of the cost effectiveness, it enables us to save almost 33% of the operation cost during the time period when the controller can be applied. Thus, the fuzzy neural network proved to be a robust and effective DO control tool, easy to integrate in a global monitoring system for cost managing.     

Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach

This article investigates metamodeling opportunities in buffer allocation and performance modeling in asynchronous assembly systems (AAS). Practical challenges to properly design these complex systems are emphasized. A critical review of various approaches in modeling and evaluation of assembly systems reported in the recently published literature, with a special emphasis on the buffer allocation problems, is given. Various applications of artificial intelligence techniques on manufacturing systems problems, particularly those related to artificial neural networks, are also reviewed. Advantages and the drawbacks of the metamodeling approach are discussed. In this context, a metamodeling application on AAS buffer design/performance modeling problems in an attempt to extend the application domain of metamodeling approach to manufacturing/assembly systems is presented. An artificial neural network (ANN) metamodel is developed for a simulation model of an AAS. The ANN and regression metamodels for each AAS are compared with respect to their deviations from the simulation results. The analysis shows that the ANN metamodels can successfully be used to model of AASs. Consequently, one concludes that practising engineers involved in assembly system design can potentially benefit from the advantages of the metamodeling approach.     

Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: A Case Study of an International Bank

The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six‐step procedure. The back‐propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.     

人工神经网络设计算法的研究

人工神经网络在计算机领域中仍然是一个有待进一步研究的问题。文中介绍了目前使用的几种不同的人工神经网络设计算法：BP算法、FP算法、多层前向网络的交叉覆盖设计算法。探讨了这些算法的应用和特点，并从原理和设计思路上进行了分析。对BP算法和交叉覆盖算法给出具体的测试结果，解释并说明了各种算法的优缺点。重点研究并介绍了目前流行的多层前向网络的交叉覆盖算法的设计思路和应用方法，测试结果证明了该方法在智能识别领域的有效性。     

Design optimization with back-propagation neural networks

A methodology with back-propagation neural network models is developed to explore the artificial neural nets (ANN) technology in the new application territory of design optimization. This design methodology could go beyond the Hopfield network model, Hopfield and Tank (1985), for combinatorial optimization problems In this approach, pattern classification with back-propagation network, the most demonstrated power of neural networks applications, is utilized to identify the boundaries of the feasible and the infeasible design regions. These boundaries enclose the multi-dimensional space within which designs satisfy all design criteria. A feedforward network is then incorporated to perform function approximation of the design objective function. This approximation is performed by training the feedforward network with objective functions evaluated at selected design sets in the feasible design regions. Additional optimum design sets in the classified feasible regions are calculated and included in the successive training sets to improve the function mapping. Iteration is continued until convergent criteria are satisfied. This paper demonstrates that the artificial neural nets technology provides a global perspective of the entire design space with good and near optimal solutions. ANN can indeed be a potential technology for design optimization.     

A critical review of the most popular types of neuro control

In this review article, the most popular types of neural network control systems are briefly introduced and their main features are reviewed. Neuro control systems are defined as control systems in which at least one artificial neural network (ANN) is directly involved in generating the control command. Initially, neural networks were mostly used to model system dynamics inversely to produce a control command which pushes the system towards a desired or reference value of the output (1989). At the next stage, neural networks were trained to track a reference model, and ANN model reference control appeared (1990). In that method, ANNs were used to extend the application of adaptive reference model control, which was a well‐known control technique. This attitude towards the extension of the application of well‐known control methods using ANNs was followed by the development of ANN model‐predictive (1991), ANN sliding mode (1994) and ANN feedback linearization (1995) techniques. As the first category of neuro controllers, inverse dynamics ANN controllers were frequently used to form a control system together with other controllers, but this attitude faded as other types of ANN control systems were developed. However, recently, this approach has been revived. In the last decade, control system designers started to use ANNs to compensate/cancel undesired or uncertain parts of systems' dynamics to facilitate the use of well‐known conventional control systems. The resultant control system usually includes two or three controllers. In this paper, applications of different ANN control systems are also addressed. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Process optimization of sedimentation self-assembly of opal photonic crystals under relative humidity-controlled environments

Minimum time of fabricating high quality of opal photonic band gap (PBG) crystal films was obtained by adjusting the relative humidity (RH)-controlled environments using the process optimization approach developed in this study. A three-stage process of silica particle (380 nm) deposition was proposed for the PBG crystal fabrication. The stop band intensity of the produced PBG film was determined using a spectrometer to represent the quality of the film. A process model of the film quality associated with the RH environments at the three stages was constructed using a design of experiment (DOE) and an artificial neural networks (ANN) methods. Results show that the quality of the PBG films was most influenced by the RH environment in the final deposition stage. The optimization approach with the built ANN model was used to estimate the optimal RH-controlled conditions to minimize the fabrication time of required PBG films. Moreover, the predicted optimal operating conditions were verified by experimental tests which results agree with the estimated quality of the PBG films.     

Artificial neural networks for the cost estimation of stamping dies

Although artificial neural networks (ANN) are more known in the field of image recognition and forecasting, cost estimation has become another emerging area in recent years. In this study, the establishment of an intelligent system was attempted for forecasting the total cost of sheet metal stamping dies. In this context, where the cost of stamping dies is estimated with a conventional approach which has been applied in the company up to now, the ANN and multiple regression analysis and the performance of the three cost-estimation models are examined. The examinations are based on the data of previous costs and use a number of critical criteria which are decided by experienced tool makers and engineers from every level of the organization of the seven companies which produce stamping dies. The comparative study reveals that the ANN system outperforms the traditional linear regression analysis model and conventional approach used for cost estimation. Thus, it is possible for firms which produce stamping dies to obtain a fairly accurate prediction with an ANN model and determined criteria.     

基于遗传算法的前向神经网络结构优化

对近几年应用遗传算法（Genetic Algorithm,GA）优化设计前向神经网络结构的研究进行了评述。指出了神经网络结构优化设计的重要性和目前各种方法存在的不足。介绍了神经网络结构设计原理和应用GA优化设计神经网络应着重考虑的两个问题：即结构表达策略和适应度函数设计。分别对近来应用GA优化设计多层感知器、径向基函数神经网络和径向基概率神经网络结构的研究进行了细致介绍和分析。指出了目前研究工作的不足和未来研究工作的发展方向。     

Control of tolerances in turning by predictive control with neural networks

Stochastic and non-deterministic influences have an effect on cutting processes and lead to an unsteady and dynamic process behaviour. Concepts for the improvement of process reliability and for the control of tolerances have to be developed in order to fulfil the increasing requirements on product quality. A concept for the improvement of manufacturing accuracy through artificial neural networks (ANN) will be presented as an example for the turning process. This ANN model makes it possible to predict the dimensional deviation caused by tool wear. Feeding this back in an open loop within the machine controller the deviation can be compensated by using an adaptive control of the depth of cut.     

人工神经网络在并行计算机集群上的设计研究

人工神经网络在集群上的并行化设计和实现能够充分发挥ANN并行处理的特点,缩短训练时间,降低算法复杂度。随着并行技术的日益成熟,在并行集群上以软硬件相结合的方式设计神经网络的重要性也不断提高。从软硬件平台的多方面讨论了并行集群技术对人工神经网络设计的支持,提出了一种SOM神经网络在并行集群上的设计方法和基础框架,并就并行集群上神经网络训练效率的问题进行了深入讨论。该方案可广泛应用于多种神经网络模型的并行计算机实现。