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1 – 10 of over 16000Peter Arcidiacono, Patrick Bayer, Federico A. Bugni and Jonathan James
Many dynamic problems in economics are characterized by large state spaces which make both computing and estimating the model infeasible. We introduce a method for approximating…
Abstract
Many dynamic problems in economics are characterized by large state spaces which make both computing and estimating the model infeasible. We introduce a method for approximating the value function of high-dimensional dynamic models based on sieves and establish results for the (a) consistency, (b) rates of convergence, and (c) bounds on the error of approximation. We embed this method for approximating the solution to the dynamic problem within an estimation routine and prove that it provides consistent estimates of the modelik’s parameters. We provide Monte Carlo evidence that our method can successfully be used to approximate models that would otherwise be infeasible to compute, suggesting that these techniques may substantially broaden the class of models that can be solved and estimated.
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Tugrul Oktay, Seda Arik, Ilke Turkmen, Metin Uzun and Harun Celik
The aim of this paper is to redesign of morphing unmanned aerial vehicle (UAV) using neural network for simultaneous improvement of roll stability coefficient and maximum…
Abstract
Purpose
The aim of this paper is to redesign of morphing unmanned aerial vehicle (UAV) using neural network for simultaneous improvement of roll stability coefficient and maximum lift/drag ratio.
Design/methodology/approach
Redesign of a morphing our UAV manufactured in Faculty of Aeronautics and Astronautics, Erciyes University is performed with using artificial intelligence techniques. For this purpose, an objective function based on artificial neural network (ANN) is obtained to get optimum values of roll stability coefficient (Clβ) and maximum lift/drag ratio (Emax). The aim here is to save time and obtain satisfactory errors in the optimization process in which the ANN trained with the selected data is used as the objective function. First, dihedral angle (φ) and taper ratio (λ) are selected as input parameters, C*lβ and Emax are selected as output parameters for ANN. Then, ANN is trained with selected input and output data sets. Training of the ANN is possible by adjusting ANN weights. Here, ANN weights are adjusted with artificial bee colony (ABC) algorithm. After adjusting process, the objective function based on ANN is optimized with ABC algorithm to get better Clβ and Emax, i.e. the ABC algorithm is used for two different purposes.
Findings
By using artificial intelligence methods for redesigning of morphing UAV, the objective function consisting of C*lβ and Emax is maximized.
Research limitations/implications
It takes quite a long time for Emax data to be obtained realistically by using the computational fluid dynamics approach.
Practical implications
Neural network incorporation with the optimization method idea is beneficial for improving Clβ and Emax. By using this approach, low cost, time saving and practicality in applications are achieved.
Social implications
This method based on artificial intelligence methods can be useful for better aircraft design and production.
Originality/value
It is creating a novel method in order to redesign of morphing UAV and improving UAV performance.
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The purpose of this paper is to present a degenerated simplex search method to optimize neural network error function. By repeatedly reflecting and expanding a simplex, the…
Abstract
Purpose
The purpose of this paper is to present a degenerated simplex search method to optimize neural network error function. By repeatedly reflecting and expanding a simplex, the centroid property of the simplex changes the location of the simplex vertices. The proposed algorithm selects the location of the centroid of a simplex as the possible minimum point of an artificial neural network (ANN) error function. The algorithm continually changes the shape of the simplex to move multiple directions in error function space. Each movement of the simplex in search space generates local minimum. Simulating the simplex geometry, the algorithm generates random vertices to train ANN error function. It is easy to solve problems in lower dimension. The algorithm is reliable and locates minimum function value at the early stage of training. It is appropriate for classification, forecasting and optimization problems.
Design/methodology/approach
Adding more neurons in ANN structure, the terrain of the error function becomes complex and the Hessian matrix of the error function tends to be positive semi‐definite. As a result, derivative based training method faces convergence difficulty. If the error function contains several local minimum or if the error surface is almost flat, then the algorithm faces convergence difficulty. The proposed algorithm is an alternate method in such case. This paper presents a non‐degenerate simplex training algorithm. It improves convergence by maintaining irregular shape of the simplex geometry during degenerated stage. A randomized simplex geometry is introduced to maintain irregular contour of a degenerated simplex during training.
Findings
Simulation results show that the new search is efficient and improves the function convergence. Classification and statistical time series problems in higher dimensions are solved. Experimental results show that the new algorithm (degenerated simplex algorithm, DSA) works better than the random simplex algorithm (RSM) and back propagation training method (BPM). Experimental results confirm algorithm's robust performance.
Research limitations/implications
The algorithm is expected to face convergence complexity for optimization problems in higher dimensions. Good quality suboptimal solution is available at the early stage of training and the locally optimized function value is not far off the global optimal solution, determined by the algorithm.
Practical implications
Traditional simplex faces convergence difficulty to train ANN error function since during training simplex can't maintain irregular shape to avoid degeneracy. Simplex size becomes extremely small. Hence convergence difficulty is common. Steps are taken to redefine simplex so that the algorithm avoids the local minimum. The proposed ANN training method is derivative free. There is no demand for first order or second order derivative information hence making it simple to train ANN error function.
Originality/value
The algorithm optimizes ANN error function, when the Hessian matrix of error function is ill conditioned. Since no derivative information is necessary, the algorithm is appealing for instances where it is hard to find derivative information. It is robust and is considered a benchmark algorithm for unknown optimization problems.
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Houlai Lin, Liang Li, Kaiqi Meng, Chunli Li, Liang Xu, Zhiliang Liu and Shibao Lu
This paper aims to develop an effective framework which combines Bayesian optimized convolutional neural networks (BOCNN) with Monte Carlo simulation for slope reliability…
Abstract
Purpose
This paper aims to develop an effective framework which combines Bayesian optimized convolutional neural networks (BOCNN) with Monte Carlo simulation for slope reliability analysis.
Design/methodology/approach
The Bayesian optimization technique is firstly used to find the optimal structure of CNN based on the empirical CNN model established in a trial and error manner. The proposed methodology is illustrated through a two-layered soil slope and a cohesive slope with spatially variable soils at different scales of fluctuation.
Findings
The size of training data suite, T, has a significant influence on the performance of trained CNN. In general, a trained CNN with larger T tends to have higher coefficient of determination (R2) and smaller root mean square error (RMSE). The artificial neural networks (ANN) and response surface method (RSM) can provide comparable results to CNN models for the slope reliability where only two random variables are involved whereas a significant discrepancy between the slope failure probability (Pf) by RSM and that predicted by CNN has been observed for slope with spatially variable soils. The RSM cannot fully capture the complicated relationship between the factor of safety (FS) and spatially variable soils in an effective and efficient manner. The trained CNN at a smaller the scale of fluctuation (λ) exhibits a fairly good performance in predicting the Pf for spatially variable soils at higher λ with a maximum percentage error not more than 10%. The BOCNN has a larger R2 and a smaller RMSE than empirical CNN and it can provide results fairly equivalent to a direct Monte Carlo Simulation and therefore serves a promising tool for slope reliability analysis within spatially variable soils.
Practical implications
A geotechnical engineer could use the proposed method to perform slope reliability analysis.
Originality/value
Slope reliability can be efficiently and accurately analyzed by the proposed framework.
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The purpose of this paper is to address the issue of optimal management of ecosystems by developing a dynamic model of strategic behavior by users/communities of an ecosystem such…
Abstract
Purpose
The purpose of this paper is to address the issue of optimal management of ecosystems by developing a dynamic model of strategic behavior by users/communities of an ecosystem such as a lake, which is subject to pollution resulting from the users. More specifically, it builds a model of two ecosystems that are spatially connected.
Design/methodology/approach
The paper uses the techniques of optimal control theory and game theory.
Findings
The paper uncovers sufficient conditions under which the analysis of the dynamic game can be converted to an optimal problem for a pseudo authority. It is shown that if the discount rate on the future is high enough relative to ecological self‐restoration parameters then multiple stable states appear. In this case, if the pollution level is high enough it is too costly in terms of what must be given up today to restore the damaged system. By using computational methods, the paper evaluates the relative strengths of lack of coordination, strength of ecosystem self‐cleaning forces, size of discount rates, etc.
Originality/value
The methodology as well as findings can help to devise an optimal management strategy over time for ecosystems.
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This paper aims to examine regime switching behaviour of the nominal exchange rate in Uganda to shed light on the necessity (as well as efficacy) of the participation of the…
Abstract
Purpose
This paper aims to examine regime switching behaviour of the nominal exchange rate in Uganda to shed light on the necessity (as well as efficacy) of the participation of the central bank market.
Design/methodology/approach
The homogenous two‐state Markov chain methodology was employed to investigate the possibility of regime changes in the nominal exchange rate. The maximum likelihood parameter estimates were obtained using the Broyden‐Fletcher‐Goldfarb‐Shanno iteration algorithm.
Findings
The results validate the expectation of the two distinct state spaces characterized as sharp and disruptive but short‐lived depreciations as well as small appreciations occurring through a long period. The central bank intervention actions are shown to be largely successful in mitigating the disruptive effects of the sharp depreciations.
Practical implications
The paper lends empirical support to the intervention actions of the Bank of Uganda. In face of the numerous disruptions to the short‐term exchange rate process, failure to intervene may cause rational panic and given the nature of investor behavior, this may quickly spread and even cause further disruptions. It is important for the central bank to send signals that these disruptions are temporary.
Originality/value
The homogenous Markov chain specification employed in this study makes it possible to avoid the pitfalls that may arise by attempting to specify a structural model for the exchange rate. In addition, inference about the different possible state spaces is made on the basis of all available information.
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Henry Egbezien Inegbedion, Emmanuel Edo Inegbedion, Eseosa David Obadiaru, Abiola John Asaleye, Adebanji Ayeni and Charity Aremu
The study examined policy improvement and cassava attractiveness. The purpose was to determine the optimum rewards using three strategies: selling of farm produce to harvesters…
Abstract
Purpose
The study examined policy improvement and cassava attractiveness. The purpose was to determine the optimum rewards using three strategies: selling of farm produce to harvesters, making wholesale of harvested outputs and retailing harvested outputs.
Design/methodology/approach
Three hundred and sixty (360) cassava farmers were surveyed in three local government areas in Edo South senatorial district of Nigeria. From their responses, probabilities were assigned to rewards for each strategy from each of the locations. Subsequently, dynamic programming was employed in data analysis. Specifically, Howard policy improvement technique was used to forecast expected rewards to cassava farmers in the three local government areas using the three strategies.
Findings
Cassava farmers in Edo South senatorial district of Edo state, Nigeria, can maximize their earnings from cassava by retailing at the local markets in Oredo and Egor local government areas and by making wholesales at Ikpoba Okha local government area. Using this policy, they will realize approximately N2360 per basin and approximately N33040 per plot of 100 × 100 ft. This will translate to N143724 per acre (4.35 plots of 100 ft2).
Research limitations/implications
Availability of storage facilities as well as technical constraints to cassava production.
Social implications
Provision of jobs to the unemployed, thereby reducing the level of unemployment in the country.
Originality/value
Suggestion of the sales strategy that will yield optimum returns to cassava farmers, using policy iteration technique, and the projected estimates of the likely turnover when the strategy is adopted. This is a point of departure from previous studies. Thus, the study used operations research methodology to model solutions, through involvement in agriculture, to Nigeria's economic/financial problems, thus making it unique. In broad terms the study demonstrates that investment in agriculture will help to reduce unemployment and enhance the country's national income.
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Himanshukumar R. Patel and Vipul A. Shah
The two-tank level control system is one of the real-world's second-order system (SOS) widely used as the process control in industries. It is normally operated under the…
Abstract
Purpose
The two-tank level control system is one of the real-world's second-order system (SOS) widely used as the process control in industries. It is normally operated under the Proportional integral and derivative (PID) feedback control loop. The conventional PID controller performance degrades significantly in the existence of modeling uncertainty, faults and process disturbances. To overcome these limitations, the paper suggests an interval type-2 fuzzy logic based Tilt-Integral-Derivative Controller (IT2TID) which is modified structure of PID controller.
Design/methodology/approach
In this paper, an optimization IT2TID controller design for the conical, noninteracting level control system is presented. Regarding to modern optimization context, the flower pollination algorithm (FPA), among the most coherent population-based metaheuristic optimization techniques is applied to search for the appropriate IT2FTID's and IT2FPID's parameters. The proposed FPA-based IT2FTID/IT2FPID design framework is considered as the constrained optimization problem. System responses obtained by the IT2FTID controller designed by the FPA will be differentiated with those acquired by the IT2FPID controller also designed by the FPA.
Findings
As the results, it was found that the IT2FTID can provide the very satisfactory tracking and regulating responses of the conical two-tank noninteracting level control system superior as compared to IT2FPID significantly under the actuator and system component faults. Additionally, statistical Z-test carried out for both the controllers and an effectiveness of the proposed IT2FTID controller is proven as compared to IT2FPID and existing passive fault tolerant controller in recent literature.
Originality/value
Application of new metaheuristic algorithm to optimize interval type-2 fractional order TID controller for nonlinear level control system with two type of faults. Also, proposed method will compare with other method and statistical analysis will be presented.
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Veni Arakelian and Efthymios G. Tsionas
In this paper we take up Bayesian inference for the consumption capital asset pricing model. The model has several econometric complications. First, it implies exact relationships…
Abstract
In this paper we take up Bayesian inference for the consumption capital asset pricing model. The model has several econometric complications. First, it implies exact relationships between asset returns and the endowment growth rate that will be rejected by all possible realizations. Second, it was thought before that it is not possible to express asset returns in closed form. We show that Labadie's (1989) solution procedure can be applied to obtain asset returns in closed form and, therefore, it is possible to give an econometric interpretation in terms of traditional measurement error models. We apply the Bayesian inference procedures to the Mehra and Prescott (1985) dataset, we provide posterior distributions of structural parameters and posterior predictive asset return distributions, and we use these distributions to assess the existence of asset returns puzzles. The approach developed here, can be used in sampling theory and Bayesian frameworks alike. In fact, in a sampling-theory context, maximum likelihood can be used in a straightforward manner.