Impact of input data on soil model calibration using Genetic Algorithms

2010 ◽  
pp. 85-90 ◽  
Keyword(s):  
Genetic Algorithms ◽  
Model Calibration ◽  
Input Data ◽  
Soil Model

Designing Unsupervised Hierarchical Fuzzy Logic Systems

2011 ◽  
pp. 456-463
Author(s):  
M. Mohammadian
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Unsupervised Learning ◽  
Input Data ◽  
Fuzzy Logic System ◽  
Fuzzy Rules ◽  
Fuzzy Logic Systems ◽  
Logic Systems ◽  
The Individual ◽  
Logic System

Systems such as robotic systems and systems with large input-output data tend to be difficult to model using mathematical techniques. These systems have typically high dimensionality and have degrees of uncertainty in many parameters. Artificial intelligence techniques such as neural networks, fuzzy logic, genetic algorithms and evolutionary algorithms have created new opportunities to solve complex systems. Application of fuzzy logic [Bai, Y., Zhuang H. and Wang, D. (2006)] in particular, to model and solve industrial problems is now wide spread and has universal acceptance. Fuzzy modelling or fuzzy identification has numerous practical applications in control, prediction and inference. It has been found useful when the system is either difficult to predict and or difficult to model by conventional methods. Fuzzy set theory provides a means for representing uncertainties. The underlying power of fuzzy logic is its ability to represent imprecise values in an understandable form. The majority of fuzzy logic systems to date have been static and based upon knowledge derived from imprecise heuristic knowledge of experienced operators, and where applicable also upon physical laws that governs the dynamics of the process. Although its application to industrial problems has often produced results superior to classical control, the design procedures are limited by the heuristic rules of the system. It is simply assumed that the rules for the system are readily available or can be obtained. This implicit assumption limits the application of fuzzy logic to the cases of the system with a few parameters. The number of parameters of a system could be large. The number of fuzzy rules of a system is directly dependent on these parameters. As the number of parameters increase, the number of fuzzy rules of the system grows exponentially. Genetic Algorithms can be used as a tool for the generation of fuzzy rules for a fuzzy logic system. This automatic generation of fuzzy rules, via genetic algorithms, can be categorised into two learning techniques, supervised and unsupervised. In this paper unsupervised learning of fuzzy rules of hierarchical and multi-layer fuzzy logic control systems are considered. In unsupervised learning there is no external teacher or critic to oversee the learning process. In other words, there are no specific examples of the function to be learned by the system. Rather, provision is made for a task-independent measure of the quality or representation that the system is required to learn. That is the system learns statistical regularities of the input data and it develops the ability to learn the feature of the input data and thereby create new classes automatically [Mohammadian, M., Nainar, I. and Kingham, M. (1997)]. To perform unsupervised learning, a competitive learning strategy may be used. The individual strings of genetic algorithms compete with each other for the “opportunity” to respond to features contained in the input data. In its simplest form, the system operates in accordance with the strategy that ‘the fittest wins and survives’. That is the individual chromosome in a population with greatest fitness ‘wins’ the competition and gets selected for the genetic algorithms operations (cross-over and mutation). The other individuals in the population then have to compete with fit individual to survive. The diversity of the learning tasks shown in this paper indicates genetic algorithm’s universality for concept learning in unsupervised manner. A hybrid integrated architecture incorporating fuzzy logic and genetic algorithm can generate fuzzy rules for problems requiring supervised or unsupervised learning. In this paper only unsupervised learning of fuzzy logic systems is considered. The learning of fuzzy rules and internal parameters in an unsupervised manner is performed using genetic algorithms. Simulations results have shown that the proposed system is capable of learning the control rules for hierarchical and multi-layer fuzzy logic systems. Application areas considered are, hierarchical control of a network of traffic light control and robotic systems. A first step in the construction of a fuzzy logic system is to determine which variables are fundamentally important. Any number of these decision variables may appear, but the more that are used, the larger the rule set that must be found. It is known [Raju, S., Zhou J. and Kisner, R. A. (1990), Raju G. V. S. and Zhou, J. (1993), Kingham, M., Mohammadian, M, and Stonier, R. J. (1998)], that the total number of rules in a system is an exponential function of the number of system variables. In order to design a fuzzy system with the required accuracy, the number of rules increases exponentially with the number of input variables and its associated fuzzy sets for the fuzzy logic system. A way to avoid the explosion of fuzzy rule bases in fuzzy logic systems is to consider Hierarchical Fuzzy Logic Control (HFLC) [Raju G. V. S. and Zhou, J. (1993)]. A learning approach based on genetic algorithms [Goldberg, D. (1989)] is discussed in this paper for the determination of the rule bases of hierarchical fuzzy logic systems.

scholarly journals Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ali Al Haidan ◽  
Osama Abu-Hammad ◽  
Najla Dar-Odeh
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Tooth Surface ◽  
Output Data ◽  
Surface Loss ◽  
Artificial Neural ◽  
High Degree

Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.

scholarly journals ROUTING OF VEHICLES USING THE METAHEURISTIC ALGORITHM

2020 ◽  
Vol 72 (4) ◽  
pp. 225-230
Author(s):  
D. Nurserik ◽  
◽  
F.R. Gusmanova ◽  
G.А. Abdulkarimova ◽  
K.S. Dalbekova ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Input Data ◽  
Optimal Solution ◽  
Combinatorial Problem ◽  
Routing Problem ◽  
Metaheuristic Methods ◽  
Short Period ◽  
Np Complete

The main goal of the proposed research is to solve the problem of vehicle routing using genetic algorithms. Vehicle Routing Problem (VRP) is an NP-complete complex combinatorial problem. With a large amount of input data in a VRP problem, it is very expensive to find the most optimal solution. Genetic algorithms offer the most optimal solution in a short period of time. This article discusses genetic algorithms based on the mechanism of evolution for finding the optimal route by metaheuristic methods. The aim of the work is to minimize the time needed to find the most acceptable optimal solution to the problem, as well as to develop metaheuristic methods.

Improving the estimates of nitrate concentrations at subsurface drained agricultural catchment scale using a new conceptual water quality model

2021 ◽  
Author(s):  
Julien Tournebize ◽  
Samy Chelil ◽  
Hocine Henine ◽  
Cedric Chaumont
Keyword(s):  
Water Quality ◽  
Soil Profile ◽  
Model Calibration ◽  
Input Data ◽  
Performance Criteria ◽  
Time Step ◽  
Data Set ◽  
Calibration And Validation ◽  
Nitrate Concentrations

<p>The agricultural source pollution, such as nutrient and pesticides, affect the quality of surface water and groundwater. The agricultural nonpoint source pollution due to the excessive land fertilization is considered by researchers and governments as a concerning and sensitive issue. At the scale of agricultural catchments, the modeling of nitrate-leaching losses has been widely addressed in several studies. However, most of developed models require a large number of input data and parameters. Some of them include a complex process of biogeochemical nitrogen process or a full agronomic module and could be computationally time-consuming. Moreover, the quality of the input data makes the model calibration less efficient.</p><p>The objective of this study is to present a new conceptual and reservoir model (SIDRA-N), developed to better access the time-variation of nitrate concentrations [NO3-] at the outlet of subsurface drainage network. The model represent a simplified scheme of subsurface flow and nitrate transfer processes in the soil profile, between the drain and the mid-drain. The soil profile is decomposed into three interconnected compartments: the first compartment represents the rapid transfer of water and nitrate through the soil macroporosity; the two other compartments describe the progressive contribution of the horizontal transfer.</p><p>The input data to the nitrate module consists on the Remaining pools of Nitrate at the Beginning of Winter season (RNBW), introduced before the winter of each hydrological year. This value should represent all biogeochemical transformations of nitrogen and agricultural practices from previous crop. This variable can explain until 80% of the total nitrate flux exported yearly. Hence, SIDRA-N model requires only two input variables: the drainage discharge and the RNBW. A set of parameters was introduced to regulate nitrate fluxes and discharge transiting through compartments to the drain outlet.</p><p>Calibration and validation (C/V) procedures are fundamental to the assessment of the performance and the robustness of water quality models. In this study, the split sample test for the model calibration and validation (C/V) was carried out using data set from Rampillon study site (355 ha, data for 6 years), located East of Paris, in France. The C/V step was performed using high frequency observations (hourly time-step) of nitrate concentrations and drainage discharge. The results showed performance criteria of KGE greater than 0.5 and RMSE less than 5 mgN/l. These results confirm the very good quality of simulations. Finally, a seasonal model calibration was implemented to observe the yearly parameter variability and ensure the model stability and consistency.</p>

Limiting GPR in a two-layer soil model via genetic algorithms

2009 ◽  
Vol 346 (8) ◽  
pp. 768-783 ◽  
Author(s):  
Baris Gursu ◽  
Melih Cevdet Ince
Keyword(s):  
Genetic Algorithms ◽  
Soil Model
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