1. Determine the minimum number of fuzzy variables required. Deciding which of the Scalar variables (boat parameters or specifications) to use is a process that involves a great deal of engineering judgment, as there is no single right answer. Each expert will have a preferred approach, and the final proof will be determined by the results and how they are excepted by other experts. I have gone through several iterations in this area and now use seven variables, Maximum Velocity, Capsize Risk, Comfort Factor, Disp/LWL ratio, SailArea/Disp ratio, Length /Beam ratio, and Length Over All.

        2. Establish the values for each fuzzy variable. Typically each fuzzy variable needs to be assigned a minimum, optimal, and maximum value. One of the more common configurations is a "trapezoidal curve", but many other shapes are possible. The values for each of these six fuzzy variables come from one of two sources, expert input or a specific boat design. Using expert input lets you construct a hypothetical template and find all the boats in the data base that are compatible with that concept, in our case, an Ideal Cruising Boat. Using inputs from a specific boat design for the fuzzy variables returns a list of boats that are similar to the original boat (no experts are needed). In either case, "Hedges" (SOMEWHAT CLOSE, CLOSE, or VERY CLOSE) can be applied to fine tune the number of boats that are compatible with each fuzzy variable. Typically, many boats will be SOMEWHAT CLOSE to a fuzzy variable while only a few will be VERY CLOSE. This ability to intensify (VERY) or dilute (SOMEWHAT) the strength of the fuzzy variable "CLOSE" gives us a powerful tool for discriminating among boat designs.

        3. Sort the data base for those boats compatible with ALL the fuzzy variables. This is done by applying the fuzzy logical AND operator, which is like a filter that selects only boats that have a high scoring Disp/LWL DOC, and a high scoring SailArea / Disp DOC, and a high scoring Capsize Risk DOC, etc., etc. Like a weak link in an anchor chain, If ANY one parameter is out of limits, the boat is not compatible with our concept of an Ideal Cruising Boat and it scores zero. For example, a boat might score high on speed but zero on low capsize risk. Fast and unsafe are NOT compatible with our concept of a good offshore boat, so the boat would score "zero".

        Fuzzy logic is a powerful new technique for ranking complex data sets and has great potential for evaluating cruising boats. The process allows us to compare boat designs by the subtle blending of six or more fundamental variables according to easily understood rules. Getting a useful set of input values from experts is not as difficult as it sounds, and even rough values will yield valuable insight into a designs basic potential. Using a specific boat as input eliminates the need for experts and can help guide a buyer towards groups of boats that are similar to the original. Both of these approaches result in valuable information, which can help sailors evaluate the suitability of a specific boat for offshore use or identify similar boats.

        The data base and fuzzy logic program can be downloaded from the Internet by pointing your browser at:

http://www1.iwvisp.com/download/pub/spreadsheet/  

(95k, Excel 97 required).