• Smart Structures and Systems
• /
• v.25 no.6
• /
• pp.765-778
• /
• 2020

The present study intends to find a proper solution for the estimation of the physical behaviors of enlarged piles through a combination of small-scale laboratory tests and a hybrid computational predictive intelligence process. In the first step, experimental program is completed considering various critical influential factors. The results of the best multilayer perceptron (MLP)-based predictive network was implemented through three mathematical-based solutions of dragonfly algorithm (DA), whale optimization algorithm (WOA), and ant lion optimization (ALO). Three proposed models, after convergence analysis, suggested excellent performance. These analyses varied based on neurons number (e.g., in the basis MLP hidden layer) and of course, the level of its complexity. The training R² results of the best hybrid structure of DA-MLP, WOA-MLP, and ALO-MLP were 0.996, 0.996, and 0.998 where the testing R² was 0.995, 0.985, and 0.998, respectively. Similarly, the training RMSE of 0.046, 0.051, and 0.034 were obtained for the training and testing datasets of DA-MLP, WOA-MLP, and ALO-MLP techniques, while the testing RMSE of 0.088, 0.053, and 0.053, respectively. This obtained result demonstrates the excellent prediction from the optimized structure of the proposed models if only population sensitivity analysis performs. Indeed, the ALO-MLP was slightly better than WOA-MLP and DA-MLP methods.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2012.05a
• /
• pp.537-537
• /
• 2012

천수방정식의 수치모형은 하천의 유량예측, 홍수범람해석, 해일 모의 등에 널리 사용되고 있고, 그 결과는 수자원 관리, 재난 대책 등 정책적인 의사결정에 있어 유용한 자료로 활용되고 있다. 이처럼 천수방정식의 수치모형은 연구목적뿐만 아니라 실생활에 있어서도 큰 영향을 미치고 있으며, 이에 보다 정확하고 효율적인 수치모형의 구축이 수리/수자원/방재분야에서 중요한 영역이 되고 있다. 본 연구는 정확하고 안정적인 수치모의를 위해 천수방정식의 수치모형에 MLP(Multi dimensional Limiting Process)기법을 적용한 후 다차원 모의 시 MLP의 수치 진동 제어 성능을 검증하고자 하였다. MLP기법은 다차원에서 수치진동을 억제할 수 있도록 개발된 기법으로, 기존 TVD 제어자(limiter)과 MLP의 차이점은 기존 제어자들이 흐름이 발생하는 셀 경계면에서 재구성된 값이 Maximum Principle을 만족시키도록 제어자를 유도하는데 반해, MLP는 셀 절점에서 Maximum Principle을 만족시키도록 제어자를 유도한다는데 있다. MLP기법은 압축성 유체를 표현하는 2, 3차원 오일러 방정식에 적용되어 기존의 제어자들에 비해 안정적이며 정확한 수치모의를 가능하게 하는 것이 검증되었다. 하지만 천수방정식에 적용된 예는 없으며, 이에 본 연구는 천수방정식에 MLP를 적용하고 천수방정식 수치모형 검증에 주로 사용되는 수치모의를 통해 MLP의 수치 진동 제어 성능을 검증하였다. 모의 결과, MLP는 2차원 천수방정식에 있어서도 기존의 제어자들과 비교하여 수치진동을 보다 잘 제어하는 것으로 판단된다. MLP의 사용으로 인해 불연속면 근처에서 정확도가 향상되었고 수치진동이 발생하지 않아 보다 안정적인 모의가 가능하였다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.32 no.2B
• /
• pp.123-130
• /
• 2012

MLP (Multi dimensional Limiting Process) is implemented to simulate shallow water flows, and its performance over conventional TVD limiters in multidimensional flows is verified through several numerical simulations. MLP was developed to control oscillations for multi-dimensional compressible flows and proved to improve accuracy, efficiency and robustness in compressible flows. In this study, we applies MLP to modeling shallow water equations(SWEs) given that the SWEs are amenable to be solved using the large range of numerical methods developed to deal with compressible flows and MLP has been yet used for SWEs. Simulation results through the benchmark tests show that MLP has favorable features such as numerical oscillation control and convergence behaviors comparable to the conventional limiters. Both numerical accuracy and stability are improved in multi-dimensional discontinuous flows.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.2
• /
• pp.1119-1122
• /
• 2005

This paper describes the implementation methods of the MLP (mulilayer perteptrons) recognizers for numerals and non-nummerals. The MLP has known to be a very efficient classifier to recognize handwritten numerals in terms of recognition accuracy, speed, and memory requirements. The MLP in the previous researches, however, focuses on the only numeral inputs and does not pay attention to non-numeral inputs with respect to recognition accuracy, rejection rates, and other characteristics. In this paper, we present some implementation methods of the MLP in the environments that numeral and non-numerals are mixed. The MLP had been developed by three methods, and investigated with three error types introduced. The experiments had been conducted on a total of about 63,000 numerals and non-numerals. The promising method to recognize numeral and non-numerals is described in terms of the three error types.

• Journal of Advanced Navigation Technology
• /
• v.27 no.5
• /
• pp.674-684
• /
• 2023

In this paper, a real-time prediction technique for gravity disturbances is proposed using a multi-layer perceptron (MLP) model. To select a suitable MLP model, 4 models with different network sizes were designed to compare the training accuracy and execution time. The MLP models were trained using the data of vehicle moving along the surface of the sea or land, including their positions and gravity disturbance. The gravity disturbances were calculated using the 2160^th degree and order EGM2008 with SHM. Among the models, MLP4 demonstrated the highest training accuracy. After training, the weights and biases of the 4 models were stored in the embedded computer of the INS to implement the MLP network. MLP4 was found to have the shortest execution time among the 4 models. These research results are expected to contribute to improving the navigation accuracy of INS through gravity disturbance compensation in the future.

• Computers and Concrete
• /
• v.34 no.2
• /
• pp.151-168
• /
• 2024

This research aimed to appraise the effectiveness of four optimization approaches - cuckoo optimization algorithm (COA), multi-verse optimization (MVO), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) - that were enhanced with an artificial neural network (ANN) in predicting the bearing capacity of shallow foundations located on cohesionless soils. The study utilized a database of 97 laboratory experiments, with 68 experiments for training data sets and 29 for testing data sets. The ANN algorithms were optimized by adjusting various variables, such as population size and number of neurons in each hidden layer, through trial-and-error techniques. Input parameters used for analysis included width, depth, geometry, unit weight, and angle of shearing resistance. After performing sensitivity analysis, it was determined that the optimized architecture for the ANN structure was 5×5×1. The study found that all four models demonstrated exceptional prediction performance: COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP. It is worth noting that the MVO-MLP model exhibited superior accuracy in generating network outputs for predicting measured values compared to the other models. The training data sets showed R² and RMSE values of (0.07184 and 0.9819), (0.04536 and 0.9928), (0.09194 and 0.9702), and (0.04714 and 0.9923) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively. Similarly, the testing data sets produced R² and RMSE values of (0.08126 and 0.07218), (0.07218 and 0.9814), (0.10827 and 0.95764), and (0.09886 and 0.96481) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively.

• Journal of KIISE:Software and Applications
• /
• v.31 no.5
• /
• pp.537-542
• /
• 2004

Multi-Layer Perceptron network has been mainly applied to many practical problems because of its nonlinear mapping ability. However the generalization ability of MLP networks may be affected by the number of hidden nodes, the initial values of weights and the training errors. These factors, if improperly chosen, may result in poor generalization ability of MLP networks. It is important to identify these factors and their interaction in order to control effectively the generalization ability of MLP networks. In this paper, we have empirically identified the factors that affect the generalization ability of MLP networks, and compared their relative effects on the generalization performance for the conventional and visualized weight selecting methods using the controller box.

• Annual Conference of KIPS
• /
• 2003.05a
• /
• pp.337-340
• /
• 2003

다층 퍼셉트론(Multi-Layer Perceptron, MLP) 구조를 이용한 비선형 적합은 실제문제에 매우 다양하게 적용되고 있다. 이때 일반화된 MLP 구조의 적합을 위해서는 은닉노드의 개수 초기 가중값 그리고 학습 회수와 같은 구조인자들을 함께 고려해야 한다. 만약 구조인자들이 부적절하게 선택되었다면 일반화된 MLP 구조의 적합효율이 매우 저하될 수 있다. 그러므로 MLP 구조에 영향을 주는 인자들의 영향을 살펴보는 것은 중요한 문제다. 이 논문에서는 제어상자(controller box)를 통한 학습결과와 더불어 MLP 구조를 일반화할 때 영향을 줄 수 있는 구조인자(factor)들의 실증분석과 이들의 상대효과를 살펴본다.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.8
• /
• pp.96-105
• /
• 1998

The aim of this paper is to propose the method of WDHMM(Weighted DHMM), using the MLP-VQ for the improvement of speaker-independent connect-digit recognition system. MLP neural-network output distribution shows a probability distribution that presents the degree of similarity between each pattern by the non-linear mapping among the input patterns and learning patterns. MLP-VQ is proposed in this paper. It generates codewords by using the output node index which can reach the highest level within MLP neural-network output distribution. Different from the old VQ, the true characteristics of this new MLP-VQ lie in that the degree of similarity between present input patterns and each learned class pattern could be reflected for the recognition model. WDHMM is also proposed. It can use the MLP neural-network output distribution as the way of weighing the symbol generation probability of DHMMs. This newly-suggested method could shorten the time of HMM parameter estimation and recognition. The reason is that it is not necessary to regard symbol generation probability as multi-dimensional normal distribution, as opposed to the old SCHMM. This could also improve the recognition ability by 14.7% higher than DHMM, owing to the increase of small caculation amount. Because it can reflect phone class relations to the recognition model. The result of my research shows that speaker-independent connected-digit recognition, using MLP-VQ and WDHMM, is 84.22%.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.7
• /
• pp.1607-1615
• /
• 2005

This Paper describes the implementation methods of MLP (mulilayer perceptrons) neural networks to recognize or reject handwritten numerals and non-nummerals. The MLP has known to be a very efficient classifier to recognize handwritten numerals in terms of recognition accuracy, speed, and memory requirements. In the previous researches, however, researchers have focused on the only numeral inputs and have not payed attention to the non-numeral inputs with respect to recognition accuracy, rejection rates, and other characteristics. In this paper, we present some implementation methods of the MLP in the environments that numeral and non-numerals are mixed. The MLPs have been developed by three methods, and investigated with three error types introduced. The experiments have been conducted on a total of 66,701 images of numerals and non-numerals. The promising method to recognize numerals and reject non-numerals has been described in terms of the three error types.