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The paper dwells on the methodology of neural-fuzzy approach to solving the problem of ship collision prevention in a heavy traffic zone. The authors present the technique of using maneuvering board to form the elements of learning sample. The authors prove that it is rational to use a neural-fuzzy system, where generation is carried out by the lattice method without clustering. The authors investigate the effect of optimization on the quality differences. The researchers define optimal membership functions that are used to generate the input linguistic variables of a neural-fuzzy system.

The authors continued their research on the development of an intelligent automatic ships pilot containing a controller based on fuzzy logic. Its features are determined by the optimizer based on a genetic algorithm. It also contains a modular unit of neural network models of ship navigation paths, as well as a neural network classifier. This paper is devoted to the description of a neural network classifier designed to classify the movement patterns of marine vessels to identify the peculiarities of the ship depending on its type and sailing conditions. The introduction of such classifier to an autopilot allows for more precise consideration of multivariate and difficult to formalize factors affecting the vessel while operating, such as varying weather conditions, irregular waves, hydrodynamic characteristics of the vessel, draft, water under the keel, rate of the vessel sailing, etc. The article outlines the technique concerning the development of a neural network classifier and the results of its computer modelling on the example of a refrigerated transport vessel type. The authors used such methods for obtaining and processing findings as spectral estimation, machine learning methods, in particular, neural network technology and computer or simulation modelling.

The paper considers accidents and potential hazards of the world chemical enterprises, and provides statistics of accidents and human casualties. The authors investigate harmful factors affecting the production process and a human-operator, showing the central role of a human in the technological process (both as a source of errors and as an active element that eliminates errors, failures and cyber attacks’ consequences). We essentially consider automated technological complex as a Human-Machine-Environment system, thus a human-system approach should be applied. The authors developed a complex of systemic components and morphological models, which describe the human-machine system in the sections required for analysis, to ensure sustainable and reliable design with initial data. The authors also propose a method and information technology for interfaces’ ergonomic assessment; the principles for adaptive interfaces design; and mathematical models and information technology to assess safety and timeliness indicators of the chemical production operators’ activities. The models are based on the principles of the functional-structural theory by Anatoly Gubinsky, Vladimir Evgrafov, Akiva Asherov, Pavel Chabaneko and others, and on the mathematical apparatus of functional networks. Further, the authors develop an optimization model for decision supporting organizing the human-machine control technology, using the criterion of minimizing losses from unreliability and unsustainability. Both the models and the information technology have undergone extensive testing, including solving the tasks of: choosing the automation level for the control process; distributing functions between operators; control algorithms design; user interface design, design of agent-managers to support the operators’ activities. The results can be used as the basis for a decision support system to ensure sustainability and reliability of automated technological systems in chemical industry.