Science is urged to win the Complexity Challenges. One strategy to reach this goal consists in developing Artificial Intelligence because the human nervous system is a prototype of Natural Complex System that can solve few Computational Complexity problems quite easily. To try to understand human intelligence at the “implementation level”, we are proposing chromogenic compounds as surrogates of natural sensory elements. Since Fuzzy logic is the best model of human ability to compute with words, two methodologies to implement Fuzzy logic by a chromogenic spirooxazine are described. Moreover, a new definition of Colourability of a chromogenic material, based on the mathematical Theory of Information, is presented.
Scientists want to comprehend and control complex systems. Their success depends on the ability to face also the challenges of the corresponding computational complexity. A promising research line is artificial intelligence (AI). In AI, fuzzy logic plays a significant role because it is a suitable model of the human capability to compute with words, which is relevant when we make decisions in complex situations. The concept of fuzzy set pervades the natural information systems (NISs), such as living cells, the immune and the nervous systems. This paper describes the fuzziness of the NISs, in particular of the human nervous system. Moreover, it traces three pathways to process fuzzy logic by molecules and their assemblies. The fuzziness of the molecular world is useful for the development of the chemical artificial intelligence (CAI). CAI will help to face the challenges that regard both the natural and the computational complexity.
The qualities of new data used in the sequential learning phase of the online sequential extreme learning machine algorithm (OS-ELM) have a significant impact on the performance of OS-ELM. This paper proposes a novel data filter mechanism for OS-ELM from the perspective of fuzziness and a fuzziness-based online sequential extreme learning machine algorithm (FOS-ELM). In FOS-ELM, when new data arrive, a fuzzy classifier first picks out the meaningful data according to the fuzziness of each sample. Specifically, the new samples with high-output fuzziness are selected and then used in sequential learning. The experimental results on eight binary classification problems and three multiclass classification problems have shown that FOS-ELM updated by the new samples with high-output fuzziness has better generalization performance than OS-ELM. Since the unimportant data are discarded before sequential learning, FOS-ELM can save more memory and have higher computational efficiency. In addition, FOS-ELM can handle data one-by-one or chunk-by-chunk with fixed or varying sizes. The relationship between the fuzziness of new samples and the model performance is also studied in this paper, which is expected to provide some useful guidelines for improving the generalization ability of online sequential learning algorithms.
Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN). The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods.
Building a high quality classifier is one of the key problems in the field of machine learning (ML) and pattern recognition. Many ML algorithms have suffered from high computational power in the presence of large scale data sets. This paper proposes a fuzziness based instance selection technique for the large data sets to increase the efficiency of supervised learning algorithms by improving the shortcomings of designing an effective intrusion detection system (IDS). The proposed methodology is dependent on a new kind of single layer feed-forward neural network (SLFN), called random weight neural network (RWNN). At the first stage, a membership vector corresponding to every training instance is obtained by using RWNN for computing the fuzziness. Secondly, the training instances (along with their fuzziness values) according to the actual class labels are grouped separately. After this, the instances having low fuzziness values in each group are extracted, which are used to build a reduced data set. The instances outputted by the proposed method are used as an input for ML classifiers, which result in reducing the learning time and also increasing the learning capability. The proposed methodology exhibits that the reduced data set can easily learn the boundaries between class labels. The most obvious finding from this study is a considerable increase in the accuracy rate with unseen examples when compared with other instance selection method, i.e., IB2. The proposed method provides the better generalization and fast learning capability. The reasonability of the proposed methodology is theoretically explained and experiments on well known ID data sets support its usefulness.
In many cases, fuzziness and randomness simultaneously appear in a system. Hybrid variable is a tool to describe this phenomena. Fuzzy random variable and random fuzzy variable are instances of hybrid variable. In order to measure hybrid event, a concept of chance measure is proposed in this paper. Furthermore, several useful properties about this measure are proved such as self-duality, subadditivity and semicontinuity. Some concepts are also presented such as chance distribution, expected value, variance, moments, critical values, entropy, distance and sequence convergences.
Affective product design aims at incorporating customers' affective needs into design variables of a new product so as to optimise customers' affective satisfaction. Faced with fierce competition in marketplaces, companies try to determine the settings in order to maximise customers' affective satisfaction with products. To achieve this, a set of customer survey data is required in order to develop a model which relates customers' affective responses to the design variables of a new product. Customer survey data are usually fuzzy since human feeling is usually fuzzy, and the relationship between customers' affective responses and design variables is usually nonlinear. However, previous research on modelling the relationship between affective response and design variables has not addressed the development of explicit models involving either nonlinearity or fuzziness. In this paper, an intelligent fuzzy regression approach is proposed to generate models which represent this nonlinear and fuzzy relationship between affective responses and design variables. In order to do this, we extend the existing work on fuzzy regression by first utilising an evolutionary algorithm to construct branches of a tree representing structures of a model where the nonlinearity of the model can be addressed. The fuzzy regression algorithm is then used to determine the fuzzy coefficients of the model. The models thus developed are explicit, and consist of fuzzy, nonlinear terms which relate affective responses to design variables. A case study of affective product design of mobile phones is used to illustrate the proposed method.
Since XML could benefit greatly from database support and more specifically from relational database systems, we study the methodology of modeling fuzzy spatiotemporal data in XML and transforming fuzzy spatiotemporal data from XML to relational databases as well. To accomplish this, we devise a fuzzy spatiotemporal data model in XML to capture the semantics of fuzzy spatiotemporal features. To allow for better and platform independent sharing of fuzzy spatiotemporal data stored in a relational format, we propose a temporal edge approach to transform fuzzy spatiotemporal XML data into relational databases. The unique feature of our approach is that no schema information is required for transformation of fuzzy spatiotemporal data. Moreover, temporal, spatial, and fuzzy features of fuzzy spatiotemporal data in XML documents are taken into consideration. Finally, the experimental results demonstrate the performance advantages of our approach. Such approach of transformation could provide a significant consolidation of the interoperability of fuzzy spatiotemporal data from XML to relational databases.
The paper studies the fuzziness measure in fuzzy rough sets. By making use of the support set of fuzzy sets, a rough membership function for fuzzy sets based on fuzzy relation is introduced. Simultaneously, a fuzziness measure of fuzzy rough sets from total mean fuzzy degree is proposed. And then, it is proved that the fuzziness measure of fuzzy rough sets, denoted by, equals to zero if the set is crisp and definable.
Fuzzy sets and systems (FSS) fill the gap between scientific theories and observable real systems and phenomena. In philosophy and history of science and technology this gap carries a great potential for epistemological discussions. A new approach in this area is Hans-Jorg Rheinberger's "historical epistemology" dealing with the concept of "experimental systems". In this paper we first summarize some facts on the theory of FSS and Computational Intelligence, then we give a brief sketch of Rheinberger's "experimental systems", "epistemic" and "technological things", and finally we propose to combine Rheinberger's approach with FSS methodologies.