本回顧簡短的描述 “Soft Computing” 的歷史，它涵蓋了籠統邏輯，類神經網路及機率邏輯的領域，用數字（通常介於0與1之間）去表權數或籠統分類以凸顯此領域之特性，此權數可對擁有多項特徵值的輸入組合找出多數合適之分類，藉以領導大部分適合的輸出組合，這些值的模組可以是一個簡單的籠統規則庫模組，一個使自己合適之類神經網路，或是一個遺傳發展的規則庫，Soft Computing的繼承比對和自我學習之自然力保存了去打破傳統二元邏輯的潛力，或許重要性更甚於平行潛力的是典型平移在Soft Comptiong引起科學自然力，題是科學理論須是精確的，現今，它被一些不可是精確的複雜系統辨認，像是經濟學、社會學或包含幾個變數的簡單的非線性系統，對於這些系統，籠統模組似乎足以做好，在其他方面，這種籠統邏輯無法被反證像傳統科學理論，它只能被證明對被給的一組測試，它似乎作的比其他模組好。

　　　　　This review briefly sketches the history of “soft computing”[15], which encompasses the fields of fuzzy logic[13], neural networks[10], and probabilistic reasoning. The field is characterized by the use of numbers (usually between 0 and 1) to represent weights or fuzzy classifications. These weights can, in parallel, find the most suitable classification of an input consisting of various feature values, thus leading to the most appropriate output. The model for combining these values can either be a simple fuzzy rule-based model, a self-adaptive neural network, or a genetically evolving rule-base. The inherent parallelism and the self-learning nature of soft computing holds the potential to break the limitations which have traditionally held back binary-logic VonNeumann machines. Perhaps even more important than the potential parallelism, however, is the paradigm shift in the nature of science which has been brought about by soft computing. Previously, scientific theories had to be precise. Now it is recognized by some scientists that it is impossible to be precise about complex systems like economics or sociology, or even fairly simple non-linear systems involving several variables. For such systems, fuzzy models cannot be disproven like traditional scientific theories. It can only be shown that one model seems to work better than another model for a given test suite.