A task decomposition approach to using neural networks for the interpretation of bioprocess data

During the course of most bioproccss development programs a large amount of process data is generated and stored. However, while these data records contain important information about the process, little or no use is made of this asset. The work described here uses a neural network approach to “learn” to recognize patterns in fermentation data. Neural networks, trained using fermentation data generated from previous runs, are then used to interpret data from a new fermentation. We propose a task decomposition approach to the problem. The approach involves decomposing the problem of bioprocess data interpretation into specific tasks. Separate neural networks are trained to perform each of these tasks which include fault diagnosis, growth phase determination and metabolic condition evaluation. These trained networks are combined into a multiple neural network hierarchy for the diagnosis of bioprocess data. The methodology is evaluated using experimental data from fed-batch, Saccharomyces cerevisiae fermentations. We argue that the task decomposition approach taken here allows for each network to develop a task specific representation and that this in turn, can lead to network activations and connection weights that are more clearly interpretable. These expert networks can now be pruned to remove nodes that do not contribute significant additional information.     

On fault isolation by neural-networks-based parameter estimation techniques

Abstract: The aim of the work is to exploit some aspects of functional approximation techniques in parameter estimation procedures applied on fault detection and isolation tasks using backpropagation neural networks as functional approximation devices. The major focus of this paper deals with the strategy used in the data selection task as applied to the determination of non-conventional process parameters, such as performance or process-efficiency indexes, which are difficult to acquire by direct measurement. The implementation and validation procedure on a real case study is carried out with the aid of the facilities supplied by commercial neural networks toolboxes, which manage databases, neural network structures and highly efficient training algorithms.     

发酵过程的多模型融合建模算法

提出了利用多模型融合技术进行发酵过程建模的新方法， 该方法能够将在线参数和离线参数同时用于建模中． 首先给出了多模型融合建模算法框架， 并描述了基于自适应模糊神经网络和模糊推理技术两个参与融合的子模型的建立方法. 采用三个非线性函数分别运用GMDH-PTSV算法、傅里叶神经网络和多模型融合建模算法进行建模精度比较. 最后给出了多模型融合建模算法在青霉素发酵过程中应用的结果．     

An integrated neuro-genetic approach incorporating the Taguchi method for product design

Product design is a multidisciplinary activity that requires the integration of concurrent engineering approaches into a design process that secures competitive advantages in product quality. In concurrent engineering, the Taguchi method has demonstrated an efficient design approach for product quality improvement. However, the Taguchi method intuitively uses parameters and levels in measuring the optimum combination of design parameter values, which might not guarantee that the final solution is the most optimal. This work proposes an integrated procedure that involves neural network training and genetic algorithm simulation within the Taguchi quality design process to aid in searching for the optimum solution with more precise design parameter values for improving the product development. The concept of fractals in computer graphics is also considered in the generation of product form alternatives to demonstrate its application in product design. The stages in the general approach of the proposed procedures include: (1) use of the Taguchi experimental design procedure, (2) analysis of the neural network and genetic algorithm process, and (3) generation of design alternatives. An electric fan design is used as an example to describe the development and explore the applicability of the proposed procedures. The results indicate that the proposed procedures could enhance the efficiency of product design efforts by approximately 7.8%. It is also expected that the proposed design procedure will provide designers with a more effective approach to product development.     

Toward the training of feed-forward neural networks with the D-optimum input sequence

The problem under consideration is to obtain a measurement schedule for training neural networks. This task is perceived as an experimental design in a given design space that is obtained in such a way as to minimize the difference between the neural network and the system being considered. This difference can be expressed in many different ways and one of them, namely, the D-optimality criterion is used in this paper. In particular, the paper presents a unified and comprehensive treatment of this problem by discussing the existing and previously unpublished properties of the optimum experimental design (OED) for neural networks. The consequences of the above properties are discussed as well. A hybrid algorithm that can be used for both the training and data development of neural networks is another important contribution of this paper. A careful analysis of the algorithm is presented and its comprehensive convergence analysis with the help of the Lyapunov method are given. The paper contains a number of numerical examples that justify the application of the OED theory for neural networks. Moreover, an industrial application example is given that deals with the valve actuator.     

基于郭涛算法的演化神经网络

提出了一种新的演化神经网络算法GTEANN,该算法基于高效的郭涛算法,同时完成在网络结构空间和权值空间的搜索,以实现前馈神经网络的自动化设计。本方法采用的编码方案直观有效,基于该编码表示,神经网络的学习过程是一个复杂的混合整实数非线性规划问题,例如杂交操作包括网络的同构和规整处理。初步实验结果表明该方法收敛,能够达到根据训练样本自动优化设计多层前馈神经网络的目的。     

Analysis of associative memories based on stability of cellular neural networks with time delay

In the paper, associative memories based on cellular neural networks with time delay are presented. In some previous papers, the relationship between cloning templates is closer and stronger. Therefore, some methods are used to make the relationship loose. First, some theories on stability of cellular neural networks are given. Then, associative memories based on cellular neural networks are given on the basis of these theories. In addition, a design procedure of associative memories is introduced. Finally, some examples are given to verify the theoretical results and design procedures.     

An unsupervised neural network approach for automatic semiconductor wafer defect inspection

Semiconductor wafer defect inspection is an important process before die packaging. The defective regions are usually identified through visual judgment with the aid of a scanning electron microscope. Dozens of people visually check wafers and hand-mark their defective regions. Consequently, potential misjudgment may be introduced due to human fatigue. In addition, the process can incur significant personnel costs. Prior work has proposed automated visual wafer defect inspection that is based on supervised neural networks. Since it requires learned patterns specific to each application, its disadvantage is the lack of product flexibility. Self-organizing neural networks (SONNs) have been proven to have the capabilities of unsupervised auto-clustering. In this paper, an automatic wafer inspection system based on a self-organizing neural network is proposed. Based on real-world data, experimental results show, with good performance, that the proposed method successfully identifies the defective regions on wafers.     

基于神经网络的滤波器

利用函数连接型网络理论,提出了一种新的基于神经网络的非线性滤波器(NNNF),证 明了NNNF的无偏性和最小方差性.将NNNF用于谷氨酸和红霉素发酵过程的状态估计, 结果表明,NNNF滤波估计值与实验结果吻合得相当好,对噪声特性无特殊要求,对初始状 态估值具有一定的鲁棒性,NNNF可利用有限的状态量测信息在线推算其它不可测量的状 态变化,为非线性生化过程的在线优化奠定了基础.     

From local neural networks to granular neural networks: A study in information granulation

In this study, we are concerned with a construction of granular neural networks (GNNs)—architectures formed as a direct result reconciliation of results produced by a collection of local neural networks constructed on a basis of individual data sets. Being cognizant of the diversity of the results produced by the collection of networks, we arrive at the concept of granular neural network, producing results in the form of information granules (rather than plain numeric entities) that become reflective of the diversity of the results generated by the contributing networks. The design of a granular neural network exploits the concept of justifiable granularity. Introduced is a performance index quantifying the quality of information granules generated by the granular neural network. This study is illustrated with the aid of machine learning data sets. The experimental results provide a detailed insight into the developed granular neural networks.     

Analysis and design of associative memories based on recurrent neural networks with linear saturation activation functions and time-varying delays

In this letter, some sufficient conditions are obtained to guarantee recurrent neural networks with linear saturation activation functions, and time-varying delays have multiequilibria located in the saturation region and the boundaries of the saturation region. These results on pattern characterization are used to analyze and design autoassociative memories, which are directly based on the parameters of the neural networks. Moreover, a formula for the numbers of spurious equilibria is also derived. Four design procedures for recurrent neural networks with linear saturation activation functions and time-varying delays are developed based on stability results. Two of these procedures allow the neural network to be capable of learning and forgetting. Finally, simulation results demonstrate the validity and characteristics of the proposed approach.     

递归神经网络的设计与应用

结合实例,给出了递归神经网络的完整设计步骤,包括网络结构的选定,学习算法的选择和网络参数的训练过程。重点研究了学习速率的初始值选取及其调整顺序。给出的递归网络的设计方法,可以适用于多种递归神经网络。     

青霉素发酵过程建模研究

青霉素发酵过程是一种具有非线性、时变性的复杂生化反应系统,由于一些生物参数在线检测困难,许多生化过程的代谢途径尚不明确,难以建立精确数学模型.而神经网络具有非线性、多变量、自学习、并行处理等特点,用于非线性系统的建模具有无可比拟的优势.因此,以青霉素发酵过程生化机理模型产生的数据为样本,训练RBF神经网络,建立了基于RBF神经网络的发酵过程模型.该模型可用于发酵过程中状态变量的估算与预测,并且可估计底物、产物、菌体浓度的变化趋势,对实际工作具有指导意义.     

多核并行脉冲神经网络模拟器的设计

脉冲神经网络属于第三代人工神经网络,它是更具有生物可解释性的神经网络模型。随着人们对脉冲神经网络不断深入地研究,不仅神经元空间结构更为复杂,而且神经网络结构规模也随之增大。以串行计算的方式,难以在个人计算机上实现脉冲神经网络的模拟仿真。为此,设计了一个多核并行的脉冲神经网络模拟器,对神经元进行编码与映射,自定义路由表解决了多核间的网络通信,以时间驱动为策略,实现核与核间的动态同步,在模拟器上进行脉冲神经网络的并行计算。以Izhikevich脉冲神经元为模型,在模拟环境下进行仿真实验,结果表明多核并行计算相比传统的串行计算在效率方面约有两倍的提升,可为类似的脉冲神经网络的模拟并行化设计提供参考。     

Nonlinear modeling of MCFC stack based on RBF neural networks identification

Modeling molten carbonate fuel cells (MCFC) is very difficult and the most existing models are based on conversation laws which are too complicated to be used to design a control system. This paper presents an application of radial basis functions (RBF) neural networks identification to develop a nonlinear temperature model of MCFC stack. The temperature characters of MCFC stack are briefly analyzed. A summary of RBF neural networks modeling of MCFC is introduced. The simulation tests reveal that it is feasible to establish the model of MCFC stack using RBF neural networks identification. The modeling process avoids using complicated differential equations to describe the stack and the neural networks model developed can be used to predict the temperature responses online which makes it possible to design online controller of MCFC stack.     

Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process

In this paper, evolutionary algorithms (EAs) are deployed for multi-objective Pareto optimal design of group method of data handling (GMDH)-type neural networks which have been used for modelling an explosive cutting process using some input–output experimental data. In this way, multi-objective EAs (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity-preserving mechanism are used for Pareto optimization of such GMDH-type neural networks. The important conflicting objectives of GMDH-type neural networks that are considered in this work are, namely, training error (TE), prediction error (PE), and number of neurons (N) of such neural networks. Different pairs of theses objective functions are selected for 2-objective optimization processes. Therefore, optimal Pareto fronts of such models are obtained in each case which exhibit the trade-off between the corresponding pair of conflicting objectives and, thus, provide different non-dominated optimal choices of GMDH-type neural networks models for explosive cutting process. Moreover, all the three objectives are considered in a 3-objective optimization process, which consequently leads to some more non-dominated choices of GMDH-type models representing the trade-offs among the training error, prediction error, and number of neurons (complexity of network), simultaneously. The overlay graphs of these Pareto fronts also reveal that the 3-objective results include those of the 2-objective results and, thus, provide more optimal choices for the multi-objective design of GMDH-type neural networks in terms of minimum training error, minimum prediction error, and minimum complexity.     

Regulation of lactic acid concentration in its bioproduction from wheat flour

Lactic acid is an important molecule for biopolymer production that can be obtained by biological processes. This work deals with the control of the lactic acid concentration in its production bioprocess using wheat flour as substrate. An adaptive control strategy for the simultaneous saccharification, proteins hydrolysis and fermentation (SSPHF) continuous process of lactic acid production is proposed in order to regulate the lactic acid concentration to the target value. The latter is determined so that the lactic acid productivity is maximized. The control strategy effectiveness and robustness are illustrated by means of experimental results.     

广义卡尔曼滤波算法在真菌发酵过程状态估计中的应用

真菌深层培养过程是一个相当复杂的生物化学过程，为了过程进行优化，需建立动力学模型对发酵过程进行模拟，而在发酵过程中存在着诸如菌类变异，设备与环境变化等不确定因素所形成的随机噪声，这类噪声是确定性模型无法预估的，为了提高数学模型的模拟精度，本文采用广义卡尔曼滤波技术进行递推滤波估计，计算结果表明，利用这种滤波处理可以改善状态变量的估计精度。     

基于神经网络的多变量发酵过程自适应控制

运用非线性系统的线性化方法与神经网络在线辨识技术，提出了一种基于神经网络 的多变量自适应控制策略．提出的控制策略，当过程模型缺乏足够的先验知识时，对多变量 非线性连续发酵过程取得了良好的控制性能．仿真结果表明，提出的自适应控制方法能够适 应过程模型的不确定性和参数的时变性，具有较强的鲁棒性．并且通过对比分析得出，基于 微分几何理论的输入输出线性化解耦控制方案，由于控制器的设计依赖于过程模型，对模型 参数的变化很敏感，应用在发酵过程的非线性控制中，控制精度较低，鲁棒性较差．     

Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet

To realize effective modeling and secure accurate prediction abilities of models for power supply for high-field magnet (PSHFM), we develop a comprehensive design methodology of information granule-oriented radial basis function (RBF) neural networks. The proposed network comes with a collection of radial basis functions, which are structurally as well as parametrically optimized with the aid of information granulation and genetic algorithm. The structure of the information granule-oriented RBF neural networks invokes two types of clustering methods such as K-Means and fuzzy C-Means (FCM). The taxonomy of the resulting information granules relates to the format of the activation functions of the receptive fields used in RBF neural networks. The optimization of the network deals with a number of essential parameters as well as the underlying learning mechanisms (e.g., the width of the Gaussian function, the numbers of nodes in the hidden layer, and a fuzzification coefficient used in the FCM method). During the identification process, we are guided by a weighted objective function (performance index) in which a weight factor is introduced to achieve a sound balance between approximation and generalization capabilities of the resulting model. The proposed model is applied to modeling power supply for high-field magnet where the model is developed in the presence of a limited dataset (where the small size of the data is implied by high costs of acquiring data) as well as strong nonlinear characteristics of the underlying phenomenon. The obtained experimental results show that the proposed network exhibits high accuracy and generalization capabilities.