In the chapter, a neuro-fuzzy-based technology strategic planning model is suggested. It is the result of an integrated and systematic hybrid of existing models presented in the literature of technology strategy planning and hybrid intelligent strategic planning. The neuro-fuzzy technique is used for modeling of technology strategies with MATLAB. The model is evaluated in Iran's petrochemical industry based on average test error and average train error which were satisfying. A list of technologies in the industry, the industry's ability in the development of technology, the attractiveness of technology in the industry, and patent indicators are identified based on experts' viewpoints. According to the location of technologies in decision matrices of technology strategies, technology strategies are proposed in three categories: research-driven, investment-driven, and knowledge-driven. Data is collected by the researchers in subsidiary companies who do research in specific fields of petrochemical industry and have the knowledge in those fields.
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A hybrid intelligent system is one that combines at least two intelligent technologies (Negnevitsky, 2002), for example, combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system. Comparison of different hybrid technologies: expert systems, fuzzy systems, neural networks, and genetic algorithms show that combination of neural network and fuzzy systems are better in some features such as knowledge representation, uncertainty tolerance, imprecision tolerance, adaptability, learning ability, explanation ability, knowledge discovery, data mining, and maintainability (Negnevitsky, 2002).
There is a range of techniques and mechanisms which is used for development of business, marketing, and technology strategies. In this paper, those methods are compared based on main common characteristics (input – techniques – models – output – system evaluation) (see table 1).