Local Linear Model Tree Algorithm Research Articles

Identification of Geochemical Anomalies Using Fractal and LOLIMOT Neuro-Fuzzy modeling in Mial Area, Central Iran

The Urumieh-Dokhtar Magmatic Arc (UDMA) is recognized as an important porphyry, disseminated, vein-type and polymetallic mineralization arc. The aim of this study is to identify and subsequently determine geochemical anomalies for exploration of Pb, Zn and Cu mineralization in Mial district situated in UDMA. Factor analysis, Concentration-Number (C-N) fractal model and Local Linear Model Tree (LOLIMOT) algorithm used for this purpose. Factor analysis utilized in recognition of the correlation between elements and their classification. This classified data used for training the LOLIMOT algorithm based on relevant elements. The results of the LOLIMOT algorithm represent anomalies in areas with no lithogeochemical samples. Although, the C-N log-log plot for target elements were generated based on stream sediment and lithogeochemical samples which could be delineated mineral potential maps of the target elements. Results obtained by the LOLIMOT and fractal modeling show that the SW and the Eastern parts of the area are proper for further exploration of Cu, Pb, and Zn.

Retraction: Using artificial neural network for forward kinematic problem of under-constrained cable robots

Cable-Driven Parallel Robot has many advantages. However, the problems of cable collision between each other and environment, the lack of proper structure and non-positive cable tension prevent the spread of them. In this work, a neural network (NN) model of under constrained cable robots is presented with external forces applied to the end-effector (EE) for computing the position of it. As in under-constrained robot’s kinematics and statics are innately coupled together, and they contemporaneously should be considered the forward kinematic problem of the robot change to an optimization problem. This approach does not require pre-knowledge of the uncertainties upper bounds and linear regression form of kinematic and dynamic models. Moreover, to ensure that all cables remain in tension, proposed control algorithm benefit the internal force concept in its structure. The main contribution of this paper has three goals. First, a method is used toward kinematic problem of the under constrained cable robot modeling using four bar linkage kinematic concept, which could be used in online control approaches for real-time purposes. Second, in order to track the position of end-effector, an online PD controller is designed by the three error criteria methods such as IAE, ISE and ITSE. Finally, as the third contribution, NN control approach is applied in order to validate the model. A model is created based on the robot’s geometry and dynamic to solve the forward kinematics problem. So, the forward kinematic problem is solved offline and used online. Moreover, an analysis of workspace is performed which discovers that the solution of the forward kinematic problem of the under-constrained cable robots is unique in this case. In addition, a modified local linear model tree algorithm for nonlinear system modelling are proposed. The results show the effectiveness of the proposed approach in modeling the under constrained cable robot.

Using artificial neural network for forward kinematic problem of under-constrained cable robots

Cable-Driven Parallel Robot has many advantages. However, the problems of cable collision between each other and environment, the lack of proper structure and non-positive cable tension prevent the spread of them. In this work, a neural network (NN) model of under constrained cable robots is presented with external forces applied to the end-effector (EE) for computing the position of it. As in under-constrained robot’s kinematics and statics are innately coupled together, and they contemporaneously should be considered the forward kinematic problem of the robot change to an optimization problem. This approach does not require pre-knowledge of the uncertainties upper bounds and linear regression form of kinematic and dynamic models. Moreover, to ensure that all cables remain in tension, proposed control algorithm benefit the internal force concept in its structure. The main contribution of this paper has three goals. First, a method is used toward kinematic problem of the under constrained cable robot modeling using four bar linkage kinematic concept, which could be used in online control approaches for real-time purposes. Second, in order to track the position of end-effector, an online PD controller is designed by the three error criteria methods such as IAE, ISE and ITSE. Finally, as the third contribution, NN control approach is applied in order to validate the model. A model is created based on the robot’s geometry and dynamic to solve the forward kinematics problem. So, the forward kinematic problem is solved offline and used online. Moreover, an analysis of workspace is performed which discovers that the solution of the forward kinematic problem of the under-constrained cable robots is unique in this case. In addition, a modified local linear model tree algorithm for nonlinear system modelling are proposed. The results show the effectiveness of the proposed approach in modeling the under constrained cable robot.

Neurofuzzy-Model-Based Unsteady Aerodynamic Computations Across Varying Freestream Conditions

This paper presents a reduced-order modeling approach based on recurrent local linear neurofuzzy models for predicting generalized aerodynamic forces in the time domain. Regarding aeroelastic applications, the unsteady aerodynamic loads are modeled as a nonlinear function of structural eigenmode-based disturbances. In contrast to established aerodynamic input/output model approaches trained by high-fidelity flow simulations, the Mach number is considered as an additional model input to account for varying freestream conditions. To train the relationship between the input parameters and the corresponding flow-induced forces, the local linear model tree algorithm is adopted in this work. The proposed method is tested exemplarily with respect to the AGARD 445.6 configuration in the subsonic, transonic, and supersonic flight regimes. It is shown that good conformity is obtained between the reduced-order model results and the respective full-order computational-fluid-dynamics solution. A further comparative analysis in the frequency domain in conjunction with a classical flutter analysis confirms the validity of the approach. Finally, the method’s potential for reducing the computational effort of aeroelastic analyses is demonstrated.

Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems

Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.

Dynamic modeling of solid oxide fuel cell stack based on local linear model tree algorithm

This paper presents a dynamic model for Solid Oxide Fuel Cell (SOFC) stack using Local Linear Model Tree (LOLIMOT) algorithm which is useful for both dynamic and steady-state studies. Most of conventional SOFC models require a large number of parameters and factors, which are difficult to be determined or estimated. In this paper, a LOLIMOT-based model, which does not require the parameters of fuel cell, is proposed for each operation mode of SOFC. In these models, decision tree-based feature selection approach is exploited to select inputs of the LOLIMOT. The proposed models are trained in a short time and they have little errors.In order to illustrate the effectiveness of the proposed models, they are applied to a simulated model of a 5-kW SOFC stack which show satisfactory results for both steady-state and dynamic studies. Furthermore, the proposed model demonstrates convincing results for real-time simulation studies.

Depth estimation of cavities from microgravity data using a new approach: the local linear model tree (LOLIMOT)

ABSTRACTIn this paper an attempt is made to estimate depth and shape parameters of subsurface cavities from microgravity data through a new soft computing approach: the locally linear model tree, known as the LOLIMOT algorithm. This method is based on locally linear neuro‐fuzzy modelling, which has recently played a successful role in various applications over non‐linear system identification.A multiple‐LOLIMOT neuro‐fuzzy model was trained separately for each of the three most common shapes of subsurface cavities: sphere, vertical cylinder and horizontal cylinder. The method was then tested for each of the cavity shapes with synthetic data. The model's suitability for application to real cases was analysed by adding random Gaussian noise to the data to simulate several levels of uncertainty and the results of LOLIMOT were compared to both multi‐layer perceptron neural network and least‐squares minimization methods. The results showed that the LOLIMOT algorithm is more robust to noise and is also more precise than either the multi‐layer perceptron or least‐squares minimization method.Furthermore, the method was tested with microgravity data over a selected site located in a major container terminal at Freeport, Grand Bahamas, to estimate cavity depth and was compared to the results achieved by least‐squares minimization and multi‐layer perceptron methods. The proposed method can estimate cavity parameters more accurately than the least‐squares minimization and multi‐layer perceptron methods.

Structure Optimization of Takagi-Sugeno Fuzzy Models

A new approach for nonlinear system identification based on Takagi-Sugeno fuzzy models is presented. The premise structure and membership functions are optimized by the LOLIMOT (local linear model tree) algorithm, see [1]. This method is extended by a subset selection technique which automatically determines the structure of the local linear models in the rule consequents. This allows to select the significant input variables for static models and additionally the determination of the dynamic orders and dead times for dynamic models. The utilized subset selection technique is the orthogonal least-squares (OLS) algorithm. It exploits the linear regression structure of the problem and thus is very fast. The applicability of the proposed approach is illustrated by the identification of a transport delay process which has operating point dependent time constants and dead times.

Exploiting Prior Knowledge in Fuzzy Model Identification of a Heat Exchanger

Abstract Intelligent automation systems integrate a great variety of methods based on models of the controlled processes, e. g. model-based control or model-based supervision and fault diagnosis. Hence, there emerges a great demand for modeling techniques suitable for the identification of nonlinear dynamic systems. Besides classical approaches, neuro/fuzzy systems appear promising due to their approximation qualities. In this contribution, it is investigated how Takagi-Sugeno type fuzzy systems can be utilized for identification of a cross-flow water/air heat exchanger. On the one hand, these models provide transparency and interpretability which allows to incorporate qualitative and quantitative prior knowledge about the plant. On the other hand, Takagi-Sugeno models can also be identified from measurement data in order to compensate for incomplete system knowledge. Data gathering requires appropriate excitation of the system. It will be shown how elementary prior knowledge can be exploited for the design of identification signals. The concept of error bars is reviewed, and it is used to roughly evaluate the model quality depending on the properties of the excitation signal. For identification from training data the local linear model tree algorithm (LOLIMOT) is applied.

Adaptive Predictive Control Based on Local Linear Fuzzy Models

Abstract In this paper, local linear fuzzy models are used for adaptive predictive control of nonlinear processes. First, an algorithm (LOLIMOT = local linear model tree algorithm) for off-line identification of Takagi-Sugeno type fuzzy models is briefly reviewed. In order to cope with time-variant nonlinear processes and also with the influence of non-measurable disturbances, an on-line adaptation of the model is performed. A recursive least-squares algorithm (RLS) is applied to update the linear parameters in the rule consequents locally. The local estimation results in low computational effort and avoids forgetting in non-active operating regimes. The effectiveness of the approach is demonstrated by simulations and by controlling the output temperature of a cross-flow heat exchanger.