Contents -- Preface -- Organization of the Chapters -- Intended Audience -- Resources for Readers -- About the Editor -- List of Contributors -- 1. Introduction -- 1.1 Historical Background of GMDH -- 1.2 Basic GMDH Algorithm -- 1.2.1 External criteria -- 1.3 GMDH-Type Neural Networks -- 1.4 Classification of GMDH Algorithms -- 1.4.1 Parametric GMDH algorithms -- 1.4.1.1 Multilayer GMDH -- 1.4.1.2 Combinatorial GMDH -- 1.4.1.3 Objective system analysis -- 1.4.2 Non-parametric GMDH algorithms -- 1.4.2.1 Objective cluster analysis (OCA) -- 1.4.2.2 Analogue complexing (AC) -- 1.4.2.3 Pointing finger clusterization algorithm -- 1.5 Rationale for GMDH in C Language -- 1.6 Available Public Software -- 1.7 Recent Developments -- 1.8 Conclusions -- References -- 2. GMDH Multilayered Iterative Algorithm (MIA) -- 2.1 Multilayered Iterative Algorithm (MIA) Networks -- 2.1.1 GMDH layers -- 2.1.2 GMDH nodes -- 2.1.3 GMDH connections -- 2.1.4 GMDH network -- 2.1.5 Regularized model selection -- 2.1.6 GMDH algorithm -- 2.2 Computer Code for GMDH-MIA -- 2.2.1 Compute a tree of quadratic polynomials -- 2.2.2 Evaluate the Ivakhnenko polynomial using the tree of polynomials generated -- 2.2.3 Compute the coefficients in the Ivakhnenko polynomial using the same tree of polynomials generated -- 2.2.4 Main program -- 2.3 Examples -- 2.3.1 Example 1 -- 2.3.2 Example 2 -- 2.4 Summary -- References -- 3. GMDH Multilayered Algorithm Using Prior Information -- 3.1 Introduction -- 3.2 Criterion Correction Algorithm -- 3.3 C++ Implementation -- 3.3.1 Building sources -- 3.4 Example -- 3.5 Conclusion -- References -- 4. Combinatorial (COMBI) Algorithm -- 4.1 The COMBI Algorithm -- 4.2 Usage of the "Structure of Functions" -- 4.3 Gradual Increase of Complexity -- 4.4 Implementation -- 4.5 Output Post-Processing -- 4.6 Output Interpretation -- 4.7 Predictive Model.

4.8 Summary -- References -- 5. GMDH Harmonic Algorithm -- 5.1 Introduction -- 5.2 Polynomial Harmonic Approximation -- 5.2.1 Polynomial, harmonic and hybrid terms -- 5.2.2 Hybrid function approximation -- 5.2.3 Need for hybrid modelling -- 5.3 GMDH Harmonic -- 5.3.1 Calculation of the non-multiple frequencies -- 5.3.2 Isolation of significant harmonics -- 5.3.3 Computing of the harmonics -- Appendix A. Derivation of the trigonometric equations -- A.1 System of equations for the weighting coefficients -- A.2 Algebraic equation for the frequencies -- A.3 The normal trigonometric equation -- References -- 6. GMDH-Based Modified Polynomial Neural Network Algorithm -- 6.1 Modified Polynomial Neural Network -- 6.2 Description of the Program of MPNN Calculation -- 6.2.1 The software framework (GMDH) -- 6.2.2 Object-oriented architecture of the software framework -- 6.2.3 Description of the program graphic interface -- 6.2.4 Description of the basic functions of the data processing interface -- 6.3 The GMDH PNN Application in Solving the Problem of an Autonomous Mobile Robot (AMR) Control -- 6.3.1 The review of GMDH applications in robotics -- 6.3.2 The application of MPNN for controlling the autonomous mobile robot -- 6.4 Application of MPNN for the Control of the Autonomous Cranberry Harvester -- 6.4.1 General project description -- 6.4.2 Formalization of the cranberry harvester control problem -- 6.4.3 Experiment results -- 6.4.3.1 Results of experiments of obstacle recognition -- 6.4.3.2 The results of experiments on the prediction of the distribution of the extreme component derivative of the objective function -- 6.4.3.3 The experiment results of AMR movement control -- 6.4.3.4 The results of group prediction based on the formation of independent local data samples for the regions with the common boundary -- 6.5 Conclusion -- References.

7. GMDH-Clustering -- 7.1 Quality Criteria for GMDH-Clustering -- 7.1.1 Introduction -- 7.1.2 Problem statement -- 7.1.3 Measures of similarity -- 7.1.4 Selection of informative attributes and the search for the best clusterization: common approach to the classification of methods -- 7.1.5 Criteria for the evaluation of clusterization quality -- 7.1.6 Objective clusterization -- 7.2 Computer Code for GMDH-Clustering Quality Criteria -- 7.3 Examples -- 7.3.1 Example 1 -- 7.3.2 Example 2 -- 7.4 Conclusion -- References -- 8. Multiagent Clustering Algorithm -- 8.1 Introduction -- 8.2 Honey Bee Swarm -- 8.3 Clustering based on the Multiagent Approach -- 8.4 Computer Code for Multiagent Clustering -- 8.4.1 Moving of agents -- 8.4.2 Natural selection -- 8.4.3 Evaluation of the conditions for objects in different cells -- 8.4.4 Main program: beeClustering -- 8.5 Examples -- 8.5.1 Example 1: Synthetic data -- 8.5.2 Example 2: Real-world problem -- 8.6 Conclusion -- References -- 9. Analogue Complexing Algorithm -- 9.1 General Introduction to Analogue Usage in Task Solutions -- 9.2 Analogue Complexing -- 9.2.1 First case: The analogue complexing GMDH algorithm -- 9.2.1.1 Computer code for a simple analogue complexing algorithm example with distance calculation in Euclidean space -- 9.2.2 Second case: Method of long-range prognosis for the air temperature over a period of ten days using robust inductive models and analogue principle (example) -- 9.2.2.1 Introduction -- 9.2.2.2 Polynomial harmonic basis of inductive prognostic models -- 9.2.2.3 Accuracy estimation of the long-range prognosis of the average air temperature for a period of ten days -- 9.2.2.4 Research of the prognosis accuracy for the average air temperature during January 2003 to December 2007 with a half-year lead-time.

9.2.2.5 Example of the long-range prognosis for the average air temperature of a ten-day period and its accuracy -- 9.2.2.6 Research of teaching data quantity on the prognosis accuracy of the average air temperature for a ten-day period -- 9.2.2.7 Summary of the example -- 9.3 Summary -- References -- 10. GMDH-Type Neural Network and Genetic Algorithm -- 10.1 Introduction -- 10.2 Background of the GMDH-type Neural Network and Genetic Algorithm -- 10.3 Description of the Genome Representation of the GMDH-GA Procedure -- 10.4 GMDH-GA for Modeling the Tool wear Problem -- 10.5 Stock Price Prediction Using the GMDH-type Neural Network -- 10.6 Summary -- References -- Index.