泛逻辑学理论——机制主义人工智能理论的逻辑基础

当前，世界各主要大国都把人工智能作为它们的国家战略。人工智能的发展正在快速改变着人类的生活方式和思想观念。在中国，有一小批研究者20多年来一直在基于辩证唯物主义潜心研究具有普适性的人工智能基础理论，包括智能的形成机制、逻辑基础、数学基础、协调机理、矛盾转化等。终于，他们各自建立了机制主义人工智能理论、泛逻辑学理论、因素空间理论、协调学、可拓学、集对分析等。其中，机制主义人工智能理论是基于智能形成机制的通用理论，它能把现有的结构主义、功能主义和行为主义三大流派有机地统一起来，使意识、情感、理智成为三位一体的关系；因素空间理论是机制主义人工智能理论的数学基础；泛逻辑学理论是机制主义人工智能理论的逻辑基础。本文介绍了泛逻辑学理论的基本思想、理论基础和应用方法，阐明它的理论意义和应用价值。特别需要指出的是，在广义概率论基础上建立的命题泛逻辑（包括刚性逻辑和柔性逻辑），可看成一个完整的命题级智能信息处理算子库，库中完整地包含了全部18种柔性信息处理模式（包括16种布尔信息处理模式），可用类型编码<a,b,e>来严格区分，用它可寻找到适合自己的信息处理算子完整簇来使用。在每一个信息处理模式中，各种不确定性的组合状态由不确定性程度属性编码<k,h,β,e>来严格区分，用它可在本信息处理模式的算子完整簇中精确选择具体的算子来使用。这表明柔性信息处理本质上是一把密码锁，它需要专门的密码<a,b,e>+<k,h,β,e>才能正常打开，不能乱点鸳鸯谱。通过只有18种模式，每种模式可以从最大算子连续变化到最小算子，已经证明了没有一个命题算子被遗漏。     

增加[Δ]算子的Gödel[n]值命题逻辑系统理论的平均真度

在添加了[Δ]算子Gödeln值命题逻辑系统中，给出了Gödel[n]值命题逻辑系统中有限理论的平均真度的定义，给出了该系统下的一些重要结论并给予证明，验证了在该系统下平均真度的一些基本性质，为进一步在该系统下研究平均真度奠定了基础。     

Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm

Data clustering is a technique for grouping similar and dissimilar data. Many clustering algorithms fail when dealing with multi-dimensional data. This paper introduces efficient methods for data clustering by Cuckoo Optimization Algorithm; called COAC and Fuzzy Cuckoo Optimization Algorithm, called FCOAC. The COA by inspire of cuckoo bird nature life tries to solve continuous problems. This algorithm clusters a large dataset to prior determined clusters numbers by this meta-heuristic algorithm and optimal the results by fuzzy logic. Firstly, the algorithm generates a random solutions equal to cuckoo population and with length dataset objects and with a cost function calculates the cost of each solution. Finally, fuzzy logic tries for the optimal solution. The performance of our algorithm is evaluated and compared with COAC, Black hole, CS, K-mean, PSO and GSA. The results show that our algorithm has better performance in comparison with them.     

A wavelet-based autoregressive fuzzy model for forecasting algal blooms

This paper proposes fuzzy models for forecasting the complex behavior of algal blooms. The models are developed through the integration of autoregressive models, the Takagi-Sugeno fuzzy model, and discrete wavelet transform algorithms. The premise parts of the proposed models are determined using the subtractive clustering technique and the consequent parts are optimized using weighted least squares. To train and validate the proposed fuzzy models, a large number of data sets were collected from Daecheong reservoir in Geum River in the Republic of Korea. The data include both water quality and hydrological variables. Total nitrogen, total phosphorous, dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, pH, air temperature, water temperature and outflow water were evaluated as input signals while chlorophyll-a was used as an output. It is demonstrated from the simulation that the proposed fuzzy models are effective in forecasting algal blooms.     

Experimental Fuzzy/Split-Range Control: Novel Strategy for Biodiesel Batch Reactor Temperature Control

With respect to environmental concerns, biofuels have been used around the world, such as biodiesel, and any improvement in the biodiesel production process is desirable. This paper proposes three different control systems based on fuzzy logic and output control signal split-range. Regulating reaction temperature with low settling time and process variable oscillation are the process control aims, and smooth output control signal for actuators protection is also desired. An automated biodiesel production prototype was used and the experimental results demonstrate that a fuzzy/split-range structure with three input variables showed the best performance, resulting in process variable regulation in set-point value with small settling time and actuators premature wearing prevention.     

Alternating temporal dominance of sensations and liking scales during the intake of a full portion of an oral nutritional supplement

Measurement of liking with consumers is often performed on a single sip or mouthful of the product and results in a single liking score that does not necessarily reflect the liking over consumption of a whole product. A dynamic approach could provide this type of information. Thus, obtaining reasons why the liking score is changing over consumption could help to gain insights for product improvement. Recently, Thomas, Visalli, Cordelle, and Schlich (2015) suggested collecting with the same consumers liking scores dynamically and Temporal Dominance of Sensations (TDS) during mono-intakes of flavored cheeses, in two independent sessions. This new type of sensory data has been analysed at individual level to identify “temporal drivers of liking” for each product.The present paper further develops this method consisting of collecting TDS and dynamic liking data in the same session and during the consumption of the full portion of a product. The products used to experiment this new method were two commercial oral nutritional supplements (ONS) assessed by 65 consumers over two lab sessions separated by one week.The protocol consisted of a TDS evaluation of 5 flavor, 2 texture and 3 taste sensations during each sip, immediately followed by a liking scale. This protocol was repeated over sips. This method, named “Alternated Temporal Drivers of Liking” (A-TDL), required that consumers record liking changes and do TDS over sips during food consumption. Results showed that one of the two ONS was more liked than the other and consumed in a larger volume resulting in a higher energy intake. In contrast, the other product resulted in a stronger thirst and presented more negative temporal drivers of liking (dry, metallic, filming) over the consecutive sips.     

PID controller for automatic voltage regulator using teaching–learning based optimization technique

The present work presents teaching–learning based optimization (TLBO) algorithm as an optimization technique in the area of tuning of the classical controller installed in automatic voltage regulator (AVR). The proposed TLBO algorithm is applied with an aim to find out the optimum value of proportional integral derivative (PID) controller gains with first order low pass filter installed in the AVR. The voltage response of the AVR system, as obtained by using the proposed TLBO based PID controller with first order low pass filter, is compared to those offered by the other algorithms reported in the recent state-of-the-art literatures. The advantage of using this control strategy may be noted by providing good dynamic responses over a wide range of system parametric variations. For on-line, off-nominal operating conditions, fast acting Sugeno fuzzy logic technique is applied to obtain the on-line dynamic responses of the studied model. Furthermore, robustness analysis is also carried out to check the performance of the designed TLBO based PID controller. An analysis, based on voltage response profile, has been investigated with the variations of the model parameters. The simulation results show that the proposed TLBO based PID controller is a significant optimization tool in the subject area of the AVR system. The essence of the present work signifies that the proposed TLBO technique maybe, successfully, applied for the AVR of power system.     

基于模糊逻辑的导弹扩展比例导引律

临近空间高超声速大机动目标的拦截制导律一直是现代制导与控制方面的一个难点和热点。运用模糊逻辑的方法，改进了扩展比例导引律，使导弹在拦截过程中能够有效跟踪导弹至目标视线转率的变化及目标的机动加速度，拦截末段视线转率收敛，实施成功拦截。     

A machine-vision inspection system for conveying attitudes of columnar objects in packing processes

This paper is a new study on developing a machine vision system for inspecting the conveying attitudes of columnar objects. The presented system consists of image pre-processing, feature extraction, and attitude diagnosis. First of all, in order to segment the objects from the background (namely image pre-processing), an improved maximum between-class variance method is proposed for searching a histogram peak and calculating a threshold value based on the statistics and probability, to solve the problems caused by the non-uniform brightness in a realistic conveyor belt. Then, an open morphological operation is used to eliminate the noise from the binary images produced in the pre-processing step. In the second step (feature extraction), the features of columnar objects are determined by four methods, edge line detecting method, intercepting method, rectangle locating method and feature statistic method. Finally, the diagnosis for the conveying attitudes of columnar objects is based on a hybrid classifier using random forests, and a fuzzy logic. The proposed system is applied to a realistic process for packing industrial explosives. The results of experiments show that the proposed system allows efficient and accurate 100% inspection for the conveying attitude, which ensures the high speed and steady operation of a packing line.