Adaptive individual handling for neural network synthesis

Neural Network Synthesis is an algorithm capable of creating and learning and artificial neural networks as well as optimizing their structures and connections. The method is based on Analytic Programming and asynchronous implementation of Self-Organising Migration Algorithm. Such approach already recorded several successful application considering practical casers of modelling and simulation. This results vindicate efforts for its further development. This paper explores a possibility to make it more effective by adaptive individual handling. The main idea is an intelligent control the process based on complexity of processed neural network structure. (C) 2016 The Authors. Published by Elsevier Ltd.Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)

Adaptive strategy for neural network synthesis constant estimation

Neural Network Synthesis is a new innovative method for an artificial neural network learning and structural optimization. It is based on two other already very successful algorithms: Analytic Programming and Self-Organizing Migration Algorithm (SOMA). The method already recorded several theoretical as well as industrial application to prove itself as a useful tool of modelling and simulation. This paper explores promising possibility to farther improve the method by application of an adaptive strategy for SOMA. The new idea of adaptive strategy is explained here and tested on a theoretical experimental case for the first time. Obtained data are statistically evaluated and ability of adaptive strategy to improve neural network synthesis is proved in conclusion

Secure high level communication protocol for CAN bus

The Controller Area Network (CAN bus) is a bus based on differential signalling originally developed for automotiv industry. The bus was later standardized under ISO 11898 and the standard describes data link layer as well as physica signalling. CAN bus allows precise settings of bus timing and sampling points, which makes it usable for varying range and baudrates. It also has a number of properties such as: message acknowledgement, collision avoidance, messag filtering and automatic retransmit of faulty messages. These properties make it suitable for many applications Furthermore, the bus is also well supported on microcontrollers and can even be found on larger SoCs. This makes th CAN bus ideal for microcontroller networks in buildings Unfortunately, the CAN protocol itself has no support for node authentication and message encryption so thes requirements has to be solved on higher layer. We present a high-level protocol for CAN bus that supports authenticatio and encryption and therefore allows usage of CAN bus in security dependent systems such as an access managemen system or in industrial automation

Context sensitive fire protection system

This paper deals with design of context sensitive fire protection system, which should be able to detect and localize fir in industrial areas, especially in large factory halls. The "context sensitive" means that the system will be able to detec the fire, but also recognize the fire type and decide whether the detected fire is intended (as a part of standard productio process) or it is an accident. Moreover, a remote controlled master stream device is part of the designed system; therefore the system can directly start fire-fighting. The main feature of this system will be extremely low level of false positiv actions and minimal collateral damage to the environment surrounding the fire

Adaptive individual handling for neural network synthesis

Neural Network Synthesis is an algorithm capable of creating and learning and artificial neural networks as well as optimizing their structures and connections. The method is based on Analytic Programming and asynchronous implementation of Self-Organising Migration Algorithm. Such approach already recorded several successful application considering practical casers of modelling and simulation. This results vindicate efforts for its further development. This paper explores a possibility to make it more effective by adaptive individual handling. The main idea is an intelligent control the process based on complexity of processed neural network structure. © 2016 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of DAAAM International Vienna

Neural Network Synthesis Dealing with classification problem

This article deals with Analytic Programming (AP) which was proven to be highly effective tool of Artificial Neural Network (ANN) synthesis and optimization. AP is used here to obtain optimal ANN which satisfactory solve given problem of classification. The algorithm is theoretically explained and successfully used to perform classification upon real life data of breast cancer diagnosis. Very simple but effective ANN is acquired as a result

Impact of Weather Inputs on Heating Plant - Agglomeration Modeling

This article describes performance of artificial neural network (ANN) oil modeling interface between a heating plant and an agglomeration. ANN perform one step ahead prediction of water temperature returned from agglomeration based on input water temperature, flow and atmospheric temperature in past 24 hours. Usage of ANN In two factual heating plant in Komorany and Detmarovice, Czech Republic. Main concern of the article is to explore possibility Of tuning ANN accuracy by additional Inputs for humidity and wind speed

Asynchronous synthesis of a neural network applied on head load prediction

This paper introduces innovative method of an artificial neural network (ANN) optimization (synthesis) by means of Analytic Programming (AP). New asynchronous implementation of Self-Organizing Migration Algorithm (SOMA), which provides effective increase of AP computing potential, is introduced here for time as well as original strategy of communication between SOMA and AP that further contribute towards efficiency in search for optimal ANN solution. The whole ANN synthesis algorithm is applied on the real case of heating plant model identification. The heating plant is located in the town of Most, Czech Republic. The method proves itself to be especially effective when formally identified non-neural parts of the heating plant model need to be made more accurate. Asynchronous distribution plays the key role here as the heating plant behavior data has to be acquired from a very large database and therefore learning of ANN may require a lot of computation time

Distributed Asynchronou Self-Organising Migration Algorithm (DASOMA) Provider

P(2C06007

Neural network synthesis

This report describes a feed forward Artificial Neural Network (ANN) synthesis via an Analytic Programming (AP) by means of the ANN creation, learning and optimization. This process encompasses four different fields: Evolutionary Algorithms, Symbolic Regression, ANN and parallel computing to successfully synthesize a suitable ANN within a reasonable time. ANN synthesis proved to be a useful and efficient tool for nonlinear modeling and its results were applied to intelligent system controlling an energetic framework of an urban agglomeration. Furthermore, the ANN synthesis proved to have the ability to synthesize smaller ANN than the Genetic Programming (GP) while simultaneously almost infinitely complex ANN can be synthesized by the application of multiple evolution loops. This process can also produce ANN with feed forward branching, which is an unavailable quality for the GP